Automatic speech recognition for under-resourced languages: A survey
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paper_title: Automatic Speech Recognition for Under-Resourced Languages: Application to Vietnamese Language

paper_content:

This paper presents our work in automatic speech recognition (ASR) in the context of under-resourced languages with application to Vietnamese. Different techniques for bootstrapping acoustic models are presented. First, we present the use of acoustic-phonetic unit distances and the potential of crosslingual acoustic modeling for under-resourced languages. Experimental results on Vietnamese showed that with only a few hours of target language speech data, crosslingual context independent modeling worked better than crosslingual context dependent modeling. However, it was outperformed by the latter one, when more speech data were available. We concluded, therefore, that in both cases, crosslingual systems are better than monolingual baseline systems. The proposal of grapheme-based acoustic modeling, which avoids building a phonetic dictionary, is also investigated in our work. Finally, since the use of sub-word units (morphemes, syllables, characters, etc.) can reduce the high out-of-vocabulary rate and improve the lack of text resources in statistical language modeling for under-resourced languages, we propose several methods to decompose, normalize and combine word and sub-word lattices generated from different ASR systems. The proposed lattice combination scheme results in a relative syllable error rate reduction of 6.6% over the sentence MAP baseline method for a Vietnamese ASR task.
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paper_title: Language-independent and language-adaptive acoustic modeling for speech recognition

paper_content:

Abstract With the distribution of speech technology products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on the question of how to port large vocabulary continuous speech recognition (LVCSR) systems in a fast and efficient way. More specifically we want to estimate acoustic models for a new target language using speech data from varied source languages, but only limited data from the target language. For this purpose, we introduce different methods for multilingual acoustic model combination and a polyphone decision tree specialization procedure. Recognition results using language-dependent, independent and language-adaptive acoustic models are presented and discussed in the framework of our GlobalPhone project which investigates LVCSR systems in 15 languages.
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paper_title: Automatic Error Recovery for Pronunciation Dictionaries.

paper_content:

In this paper, we present our latest investigations on pronunciation modeling and its impact on ASR. We propose completely automatic methods to detect, remove, and substitute inconsistent or flawed entries in pronunciation dictionaries. The experiments were conducted on different tasks, namely (1) word-pronunciation pairs from the Czech, English, French, German, Polish, and Spanish Wiktionary [1], a multilingual wiki-based open content dictionary, (2) our GlobalPhone Hausa pronunciation dictionary [2], and (3) pronunciations to complement our Mandarin-English SEAME code-switch dictionary [3]. In the final results, we fairly observed on average an improvement of 2.0% relative in terms of word error rate and even 27.3% for the case of English Wiktionary word-pronunciation pairs.
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paper_title: Unsupervised segmentation of words into morphemes – Morpho Challenge 2005: Application to automatic speech recognition

paper_content:

Within the EU Network of Excellence PASCAL, a challenge was organized to design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language, morphemes. Ideally, these are basic vocabulary units suitable for different tasks, such as speech and text understanding, machine translation, information retrieval, and statistical language modeling. Twelve research groups participated in the challenge and had submitted segmentation results obtained by their algorithms. In this paper, we evaluate the application of these segmentation algorithms to large vocabulary speech recognition using statistical n-gram language models based on the proposed word segments instead of entire words. Experiments were done for two agglutinative and morphologically rich languages: Finnish and Turkish. We also investigate combining various segmentations to improve the performance of the recognizer. Index Terms: speech recognition, language modelling, morphemes, unsupervised learning.
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paper_title: Multilingual large vocabulary speech recognition: the European SQALE project

paper_content:

Abstract This paper describes the S qale project in which the ARPA large vocabulary evaluation paradigm was adapted to meet the needs of European multilingual speech recognition development. It involved establishing a framework for sharing training and test materials, defining common protocols for training and testing systems, developing systems, running an evaluation and analysing the results. The specifically multilingual issues addressed included the impact of the language on corpora and test set design, transcription issues, evaluation metrics, recognition system design, cross-system and cross-language performance, and results analysis. The project started in December 1993 and finished in September 1995. The paper describes the evaluation framework and the results obtained. The overall conclusions of the project were that the same general approach to recognition system design is applicable to all the languages studied although there were some language specific problems to solve. It was found that the evaluation paradigm used within ARPA could be used within the European context with little difficulty and the consequent sharing amongst the sites of training and test materials and language-specific expertise was highly beneficial.
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paper_title: A multilingual phoneme and model set: toward a universal base for automatic speech recognition

paper_content:

The amount of time, effort and expense that is required to incorporate a new language into an ASR system is extensive. It also is usually not possible to provide more than one language for speech recognition per system. The Core Technology Group of Conversant Voice Information Systems, Bell Laboratories has developed a multilingual phoneme and model set (MPMS) that is being used as a base for all telephone-based ASR continuous speech systems being developed in new languages. With a unified set such as the MPMS, it is possible that multiple languages could be available on one system. While the idea of a multilingual phoneme model set is not new, there has been no work that has used a large, telephone-based database consisting of continuous speech samples in more than two languages, obtains commercially acceptable word recognition rates, and that is ready to be marketed. Our system's phoneme set represents six different languages; we have built models based on three languages and tested them using two other languages (for which there were no models); and we have achieved very acceptable word recognition rates of better than 92% (field accuracy). These languages can be incorporated into an existing speech recognition system, available for customers.
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paper_title: Language-independent and language-adaptive acoustic modeling for speech recognition

paper_content:

Abstract With the distribution of speech technology products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on the question of how to port large vocabulary continuous speech recognition (LVCSR) systems in a fast and efficient way. More specifically we want to estimate acoustic models for a new target language using speech data from varied source languages, but only limited data from the target language. For this purpose, we introduce different methods for multilingual acoustic model combination and a polyphone decision tree specialization procedure. Recognition results using language-dependent, independent and language-adaptive acoustic models are presented and discussed in the framework of our GlobalPhone project which investigates LVCSR systems in 15 languages.
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paper_title: Dynamic Bayesian network based speech recognition with pitch and energy as auxiliary variables

paper_content:

Pitch and energy are two fundamental features describing speech, having importance in human speech recognition. However, when incorporated as features in automatic speech recognition (ASR), they usually result in a significant degradation on recognition performance due to the noise inherent in estimating or modeling them. We show experimentally how this can be corrected by either conditioning the emission distributions upon these features or by marginalizing out these features in recognition. Since to do this is not obvious with standard hidden Markov models (HMMs), this work has been performed in the framework of dynamic Bayesian networks (DBNs), resulting in more flexibility in defining the topology of the emission distributions and in specifying whether variables should be marginalized out.
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paper_title: Cross-lingual Portability of MLP-Based Tandem Features – A Case Study for English and Hungarian

paper_content:

One promising approach for building ASR systems for lessresourced languages is cross-lingual adaptation. Tandem ASR is particularly well suited to such adaptation, as it includes two cascaded modelling steps: feature extraction using multi-layer perceptrons (MLPs), followed by modelling using a standard HMM. The language-specific tuning can be performed by adjusting the HMM only, leaving the MLP untouched. Here we examine the portability of feature extractor MLPs between an Indo-European (English) and a Finno-Ugric (Hungarian) language. We present experiments which use both conventional phone-posterior and articulatory feature (AF) detector MLPs, both trained on a much larger quantity of (English) data than the monolingual (Hungarian) system. We find that the cross-lingual configurations achieve similar performance to the monolingual system, and that, interestingly, the AF detectors lead to slightly worse performance, despite the expectation that they should be more language-independent than phone-based MLPs. However, the cross-lingual system outperforms all other configurations when the English phone MLP is adapted on the Hungarian data.
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paper_title: Speech recognition system based improved DTW algorithm

paper_content:

Introduced the design and implementation of isolated word speech recognition system with application of the improved dynamic time warping (DTW) algorithm. Experimental results show that the use of the improved DTW algorithm effectively reduces the amount of data to be processed and the recognition time, improved the system speed. The algorithm showed more obvious advantage as the number of speech signal to be recognized increased.
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paper_title: Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription

paper_content:

We investigate the potential of Context-Dependent Deep-Neural-Network HMMs, or CD-DNN-HMMs, from a feature-engineering perspective. Recently, we had shown that for speaker-independent transcription of phone calls (NIST RT03S Fisher data), CD-DNN-HMMs reduced the word error rate by as much as one third—from 27.4%, obtained by discriminatively trained Gaussian-mixture HMMs with HLDA features, to 18.5%—using 300+ hours of training data (Switchboard), 9000+ tied triphone states, and up to 9 hidden network layers.
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paper_title: Multilingual large vocabulary speech recognition: the European SQALE project

paper_content:

Abstract This paper describes the S qale project in which the ARPA large vocabulary evaluation paradigm was adapted to meet the needs of European multilingual speech recognition development. It involved establishing a framework for sharing training and test materials, defining common protocols for training and testing systems, developing systems, running an evaluation and analysing the results. The specifically multilingual issues addressed included the impact of the language on corpora and test set design, transcription issues, evaluation metrics, recognition system design, cross-system and cross-language performance, and results analysis. The project started in December 1993 and finished in September 1995. The paper describes the evaluation framework and the results obtained. The overall conclusions of the project were that the same general approach to recognition system design is applicable to all the languages studied although there were some language specific problems to solve. It was found that the evaluation paradigm used within ARPA could be used within the European context with little difficulty and the consequent sharing amongst the sites of training and test materials and language-specific expertise was highly beneficial.
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paper_title: Recurrent neural network based language model

paper_content:

A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition
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paper_title: Woefzela - an open-source platform for ASR data collection in the developing world

paper_content:

This project was made possible through the support of the South ::: African National Centre for Human Language Technology, an ::: initiative of the South African Department of Arts and Culture. ::: The authors would also like to thank Pedro Moreno, Thad ::: Hughes and Ravindran Rajakumar of Google Research for valuable ::: inputs at various stages of this work.
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paper_title: SWITCHBOARD: telephone speech corpus for research and development

paper_content:

SWITCHBOARD is a large multispeaker corpus of conversational speech and text which should be of interest to researchers in speaker authentication and large vocabulary speech recognition. About 2500 conversations by 500 speakers from around the US were collected automatically over T1 lines at Texas Instruments. Designed for training and testing of a variety of speech processing algorithms, especially in speaker verification, it has over an 1 h of speech from each of 50 speakers, and several minutes each from hundreds of others. A time-aligned word for word transcription accompanies each recording. >
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paper_title: Toward better crowdsourced transcription: Transcription of a year of the Let's Go Bus Information System data

paper_content:

Transcription is typically a long and expensive process. In the last year, crowdsourcing through Amazon Mechanical Turk (MTurk) has emerged as a way to transcribe large amounts of speech. This paper presents a two-stage approach for the use of MTurk to transcribe one year of Let's Go Bus Information System data, corresponding to 156.74 hours (257,658 short utterances). This data was made available for the Spoken Dialog Challenge 2010 [1]1. While others have used a one stage approach, asking workers to label, for example, words and noises in the same pass, the present approach is closer to what expert transcribers do, dividing one complicated task into several less complicated ones with the goal of obtaining a higher quality transcript. The two stage approach shows better results in terms of agreement with experts and the quality of acoustic modeling. When “gold-standard” quality control is used, the quality of the transcripts comes close to NIST published expert agreement, although the cost doubles.
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paper_title: Cross-lingual portability of Chinese and english neural network features for French and German LVCSR

paper_content:

This paper investigates neural network (NN) based cross-lingual probabilistic features. Earlier work reports that intra-lingual features consistently outperform the corresponding cross-lingual features. We show that this may not generalize. Depending on the complexity of the NN features, cross-lingual features reduce the resources used for training —the NN has to be trained on one language only— without any loss in performance w.r.t. word error rate (WER). To further investigate this inconsistency concerning intra- vs. cross-lingual neural network features, we analyze the performance of these features w.r.t. the degree of kinship between training and testing language, and the amount of training data used. Whenever the same amount of data is used for NN training, a close relationship between training and testing language is required to achieve similar results. By increasing the training data the relationship becomes less, as well as changing the topology of the NN to the bottle neck structure. Moreover, cross-lingual features trained on English or Chinese improve the best intra-lingual system for German up to 2% relative in WER and up to 3% relative for French and achieve the same improvement as for discriminative training. Moreover, we gain again up to 8% relative in WER by combining intra- and cross-lingual systems.
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paper_title: Cross-lingual Portability of MLP-Based Tandem Features – A Case Study for English and Hungarian

paper_content:

One promising approach for building ASR systems for lessresourced languages is cross-lingual adaptation. Tandem ASR is particularly well suited to such adaptation, as it includes two cascaded modelling steps: feature extraction using multi-layer perceptrons (MLPs), followed by modelling using a standard HMM. The language-specific tuning can be performed by adjusting the HMM only, leaving the MLP untouched. Here we examine the portability of feature extractor MLPs between an Indo-European (English) and a Finno-Ugric (Hungarian) language. We present experiments which use both conventional phone-posterior and articulatory feature (AF) detector MLPs, both trained on a much larger quantity of (English) data than the monolingual (Hungarian) system. We find that the cross-lingual configurations achieve similar performance to the monolingual system, and that, interestingly, the AF detectors lead to slightly worse performance, despite the expectation that they should be more language-independent than phone-based MLPs. However, the cross-lingual system outperforms all other configurations when the English phone MLP is adapted on the Hungarian data.
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paper_title: The language-independent bottleneck features

paper_content:

In this paper we present novel language-independent bottleneck (BN) feature extraction framework. In our experiments we have used Multilingual Artificial Neural Network (ANN), where each language is modelled by separate output layer, while all the hidden layers jointly model the variability of all the source languages. The key idea is that the entire ANN is trained on all the languages simultaneously, thus the BN-features are not biased towards any of the languages. Exactly for this reason, the final BN-features are considered as language independent. In the experiments with GlobalPhone database, we show that Multilingual BN-features consistently outperform Monolingual BN-features. Also, cross-lingual generalization is evaluated, where we train on 5 source languages and test on 3 other languages. The results show that the ANN can produce very good BN-features even for unseen languages, in some cases even better than if we trained the ANN on the target language only.
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paper_title: Tandem connectionist feature extraction for conventional HMM systems

paper_content:

Hidden Markov model speech recognition systems typically use Gaussian mixture models to estimate the distributions of decorrelated acoustic feature vectors that correspond to individual subword units. By contrast, hybrid connectionist-HMM systems use discriminatively-trained neural networks to estimate the probability distribution among subword units given the acoustic observations. In this work we show a large improvement in word recognition performance by combining neural-net discriminative feature processing with Gaussian-mixture distribution modeling. By training the network to generate the subword probability posteriors, then using transformations of these estimates as the base features for a conventionally-trained Gaussian-mixture based system, we achieve relative error rate reductions of 35% or more on the multicondition Aurora noisy continuous digits task.
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paper_title: Integrating Thai grapheme based acoustic models into the ML-MIX framework - For language independent and cross-language ASR

paper_content:

Grapheme based speech recognition is a powerful tool for rapidly creating automatic speech recognition (ASR) systems in new languages. For purposes of language independent or cross language speech recognition it is necessary to identify similar models in the different languages involved. For phoneme based multilingual ASR systems this is usually achieved with the help of a language independent phoneme set and the corresponding phoneme identities in the different languages. For grapheme based multilingual ASR systems this is only possible when there is an overlap in graphemes of the different scripts involved. Often this is not the case, as for example for Thai which graphemes does not have any overlap with the graphemes of the languages that we used for multilingual grapheme based ASR in the past. In order to be able to apply our multilingual grapheme model to Thai, and in order to incorporate Thai into our multilingual recognizer, we examined and evaluated a number of data driven distance measures between the multilingual grapheme models. For our purposes distance measures that rely directly on the parameters of the models, such as the Kullback-Leibler and the Bhatthacharya distance yield the best performance.
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paper_title: Data-driven posterior features for low resource speech recognition applications

paper_content:

In low resource settings, with very few hours of training data, state-of-the-art speech recognition systems that require large amounts of task specific training data perform very poorly. We address this issue by building data-driven speech recognition front-ends on significant amounts of task independent data from different languages and genres collected in similar acoustic conditions as the data in the low resource scenario. We show that features derived from these trained front-ends perform significantly better and can alleviate the effect of reduced task specific training data in low resource settings. The proposed features provide a absolute improvement of about 12% (18% relative) in an low-resource LVCSR setting with only one hour of training data. We also demonstrate the usefulness of these features for zero-resource speech applications like spoken term discovery, which operate without any transcribed speech to train systems. The proposed features provide significant gains over conventional acoustic features on various information retrieval metrics for this task.
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paper_title: Automatic Speech Recognition for Under-Resourced Languages: Application to Vietnamese Language

paper_content:

This paper presents our work in automatic speech recognition (ASR) in the context of under-resourced languages with application to Vietnamese. Different techniques for bootstrapping acoustic models are presented. First, we present the use of acoustic-phonetic unit distances and the potential of crosslingual acoustic modeling for under-resourced languages. Experimental results on Vietnamese showed that with only a few hours of target language speech data, crosslingual context independent modeling worked better than crosslingual context dependent modeling. However, it was outperformed by the latter one, when more speech data were available. We concluded, therefore, that in both cases, crosslingual systems are better than monolingual baseline systems. The proposal of grapheme-based acoustic modeling, which avoids building a phonetic dictionary, is also investigated in our work. Finally, since the use of sub-word units (morphemes, syllables, characters, etc.) can reduce the high out-of-vocabulary rate and improve the lack of text resources in statistical language modeling for under-resourced languages, we propose several methods to decompose, normalize and combine word and sub-word lattices generated from different ASR systems. The proposed lattice combination scheme results in a relative syllable error rate reduction of 6.6% over the sentence MAP baseline method for a Vietnamese ASR task.
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paper_title: Pooling ASR data for closely related languages

paper_content:

Proceedings of the Workshop on Spoken Languages Technologies for Under-Resourced Languages (SLTU 2010), Penang, Malaysia, May 2010
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paper_title: Boosting attribute and phone estimation accuracies with deep neural networks for detection-based speech recognition

paper_content:

Generation of high-precision sub-phonetic attribute (also known as phonological features) and phone lattices is a key frontend component for detection-based bottom-up speech recognition. In this paper we employ deep neural networks (DNNs) to improve detection accuracy over conventional shallow MLPs (multi-layer perceptrons) with one hidden layer. A range of DNN architectures with five to seven hidden layers and up to 2048 hidden units per layer have been explored. Training on the SI84 and testing on the Nov92 WSJ data, the proposed DNNs achieve significant improvements over the shallow MLPs, producing greater than 90% frame-level attribute estimation accuracies for all 21 attributes tested for the full system. On the phone detection task, we also obtain excellent frame-level accuracy of 86.6%. With this level of high-precision detection of basic speech units we have opened the door to a new family of flexible speech recognition system design for both top-down and bottom-up, lattice-based search strategies and knowledge integration.
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paper_title: Rapid building of an ASR system for Under-Resourced Languages based on Multilingual Unsupervised Training

paper_content:

This paper presents our work on rapid language adaptation of acoustic models based on multilingual cross-language bootstrapping and unsupervised training. We used Automatic Speech Recognition (ASR) systems in the six source languages English, French, German, Spanish, Bulgarian and Polish to build from scratch an ASR system for Vietnamese, an underresourced language. System building was performed without using any transcribed audio data by applying three consecutive steps, i.e. cross-language transfer, unsupervised training based on the “multilingual A-stabil” confidence score [1], and bootstrapping. We investigated the correlation between performance of “multilingual A-stabil” and the number of source languages and improved the performance of “multilingual A-stabil” by applying it at the syllable level. Furthermore, we showed that increasing the amount of source language ASR systems for the multilingual framework results in better performance of the final ASR system in the target language Vietnamese. The final Vietnamese recognition system has a Syllable Error Rate (SyllER) of 16.8% on the development set and 16.1% on the evaluation set. Index Terms: rapid language adaptation of ASR, unsupervised training, multilingual A-Stabil
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paper_title: Using different acoustic, lexical and language modeling units for ASR of an under-resourced language - Amharic

paper_content:

State-of-the-art large vocabulary continuous speech recognition systems use mostly phone based acoustic models (AMs) and word based lexical and language models. However, phone based AMs are not efficient in modeling long-term temporal dependencies and the use of words in lexical and language models leads to out-of-vocabulary (OOV) problem, which is a serious issue for morphologically rich languages. This paper presents the results of our contributions on the use of different units for acoustic, lexical and language modeling for an under-resourced language (Amharic spoken in Ethiopia). Triphone, Syllable and hybrid (syllable-phone) units have been investigated for acoustic modeling. Word and morphemes have been investigated for lexical and language modeling. We have also investigated the use of longer (syllable) acoustic units and shorter (morpheme) lexical as well as language modeling units in a speech recognition system. Although hybrid AMs did not bring much improvement over context dependent syllable based recognizers in speech recognition performance with word based lexical and language model (i.e. word based speech recognition), we observed a significant word error rate (WER) reduction compared to triphone-based systems in morpheme-based speech recognition. Syllable AMs also led to a WER reduction over the triphone-based systems both in word based and morpheme based speech recognition. It was possible to obtain a 3% absolute WER reduction as a result of using syllable acoustic units in morpheme-based speech recognition. Overall, our result shows that syllable and hybrid AMs are best fitted in morpheme-based speech recognition.
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paper_title: Universal attribute characterization of spoken languages for automatic spoken language recognition

paper_content:

We propose a novel universal acoustic characterization approach to spoken language recognition (LRE). The key idea is to describe any spoken language with a common set of fundamental units that can be defined ''universally'' across all spoken languages. In this study, speech attributes, such as manner and place of articulation, are chosen to form this unit inventory and used to build a set of language-universal attribute models with data-driven modeling techniques. The vector space modeling approach to LRE is adopted, where a spoken utterance is first decoded into a sequence of attributes independently of its language. Then, a feature vector is generated by using co-occurrence statistics of manner or place units, and the final LRE decision is implemented with a vector space language classifier. Several architectural configurations will be studied, and it will be shown that best performance is attained using a maximal figure-of-merit language classifier. Experimental evidence not only demonstrates the feasibility of the proposed techniques, but it also shows that the proposed technique attains comparable performance to standard approaches on the LRE tasks investigated in this work when the same experimental conditions are adopted.
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paper_title: Language-independent and language-adaptive acoustic modeling for speech recognition

paper_content:

Abstract With the distribution of speech technology products all over the world, the portability to new target languages becomes a practical concern. As a consequence our research focuses on the question of how to port large vocabulary continuous speech recognition (LVCSR) systems in a fast and efficient way. More specifically we want to estimate acoustic models for a new target language using speech data from varied source languages, but only limited data from the target language. For this purpose, we introduce different methods for multilingual acoustic model combination and a polyphone decision tree specialization procedure. Recognition results using language-dependent, independent and language-adaptive acoustic models are presented and discussed in the framework of our GlobalPhone project which investigates LVCSR systems in 15 languages.
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paper_title: Syllable-Based and Hybrid Acoustic Models for Amharic Speech Recognition

paper_content:

This paper presents the results of our experiments on the use of hybrid acoustic units in speech recognition and the use of syllable and hybrid acoustic models (AM) in morphemebased speech recognition. Although hybrid AMs did not bring improvement in speech recognition performance when words are used as dictionary entries and units in a language model (LM), we observed a significant word error rate (WER) reduction (compared to triphone-based systems) in morpheme-based speech recognition. Syllable AMs also led to a significant WER reduction over the triphone-based systems. It was possible to obtain a 3% absolute WER reduction as a result of using syllable acoustic units. Generally, our result shows that syllable and hybrid AMs are best fitted in morpheme-based speech recognition.
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paper_title: Discriminative pronunciation learning for speech recognition for resource scarce languages

paper_content:

In this paper, we describe a method to create speech recognition capability for small vocabularies in resource-scarce languages. By resource-scarce languages, we mean languages that have a small or economically disadvantaged user base which are typically ignored by the commercial world. We use a high-quality well-trained speech recognizer as our baseline to remove the dependence on large audio data for an accurate acoustic model. Using cross-language phoneme mapping, the baseline recognizer effectively recognizes words in our target language. We automate the generation of pronunciations and generate a set of initial pronunciations for each word in the vocabulary. Next, we remove potential conflicts in word recognition by discriminative training.
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paper_title: Automatic Speech Recognition for Under-Resourced Languages: Application to Vietnamese Language

paper_content:

This paper presents our work in automatic speech recognition (ASR) in the context of under-resourced languages with application to Vietnamese. Different techniques for bootstrapping acoustic models are presented. First, we present the use of acoustic-phonetic unit distances and the potential of crosslingual acoustic modeling for under-resourced languages. Experimental results on Vietnamese showed that with only a few hours of target language speech data, crosslingual context independent modeling worked better than crosslingual context dependent modeling. However, it was outperformed by the latter one, when more speech data were available. We concluded, therefore, that in both cases, crosslingual systems are better than monolingual baseline systems. The proposal of grapheme-based acoustic modeling, which avoids building a phonetic dictionary, is also investigated in our work. Finally, since the use of sub-word units (morphemes, syllables, characters, etc.) can reduce the high out-of-vocabulary rate and improve the lack of text resources in statistical language modeling for under-resourced languages, we propose several methods to decompose, normalize and combine word and sub-word lattices generated from different ASR systems. The proposed lattice combination scheme results in a relative syllable error rate reduction of 6.6% over the sentence MAP baseline method for a Vietnamese ASR task.
---
paper_title: Integrating Thai grapheme based acoustic models into the ML-MIX framework - For language independent and cross-language ASR

paper_content:

Grapheme based speech recognition is a powerful tool for rapidly creating automatic speech recognition (ASR) systems in new languages. For purposes of language independent or cross language speech recognition it is necessary to identify similar models in the different languages involved. For phoneme based multilingual ASR systems this is usually achieved with the help of a language independent phoneme set and the corresponding phoneme identities in the different languages. For grapheme based multilingual ASR systems this is only possible when there is an overlap in graphemes of the different scripts involved. Often this is not the case, as for example for Thai which graphemes does not have any overlap with the graphemes of the languages that we used for multilingual grapheme based ASR in the past. In order to be able to apply our multilingual grapheme model to Thai, and in order to incorporate Thai into our multilingual recognizer, we examined and evaluated a number of data driven distance measures between the multilingual grapheme models. For our purposes distance measures that rely directly on the parameters of the models, such as the Kullback-Leibler and the Bhatthacharya distance yield the best performance.
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paper_title: Grapheme based speech recognition

paper_content:

Large vocabulary speech recognition systems traditionally represent words in terms of subword units, usually phonemes. This paper investigates the potential of graphemes acting as subunits. In order to develop context dependent grapheme based speech recognizers several decision tree based clustering procedures are performed and compared to each other. Grapheme based speech recognizers in three languages - English, German, and Spanish - are trained and compared to their phoneme based counterparts. The results show that for languages with a close grapheme-to-phoneme relation, grapheme based modeling is as good as the phoneme based one. Furthermore, multilingual grapheme based recognizers are designed to investigate whether grapheme based information can be successfully shared among languages. Finally, some bootstrapping experiments for Swedish were performed to test the potential for rapid language deployment.
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paper_title: Automatic Error Recovery for Pronunciation Dictionaries.

paper_content:

In this paper, we present our latest investigations on pronunciation modeling and its impact on ASR. We propose completely automatic methods to detect, remove, and substitute inconsistent or flawed entries in pronunciation dictionaries. The experiments were conducted on different tasks, namely (1) word-pronunciation pairs from the Czech, English, French, German, Polish, and Spanish Wiktionary [1], a multilingual wiki-based open content dictionary, (2) our GlobalPhone Hausa pronunciation dictionary [2], and (3) pronunciations to complement our Mandarin-English SEAME code-switch dictionary [3]. In the final results, we fairly observed on average an improvement of 2.0% relative in terms of word error rate and even 27.3% for the case of English Wiktionary word-pronunciation pairs.
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paper_title: Wiktionary as a Source for Automatic Pronunciation Extraction

paper_content:

In this paper, we analyze whether dictionaries from the World Wide Web which contain phonetic notations, may support the rapid creation of pronunciation dictionaries within the speech recognition and speech synthesis system building process. As a representative dictionary, we selected Wiktionary [1] since it is at hand in multiple languages and, in addition to the definitions of the words, many phonetic notations in terms of the International Phonetic Alphabet (IPA) are available. Given word lists in four languages English, French, German, and Spanish, we calculated the percentage of words with phonetic notations in Wiktionary. Furthermore, two quality checks were performed: First, we compared pronunciations from Wiktionary to pronunciations from dictionaries based on the GlobalPhone project, which had been created in a rule-based fashion and were manually cross-checked [2]. Second, we analyzed the impact of Wiktionary pronunciations on automatic speech recognition (ASR) systems. French Wiktionary achieved the best pronunciation coverage, containing 92.58% phonetic notations for the French GlobalPhone word list as well as 76.12% and 30.16% for country and international city names. In our ASR systems evaluation, the Spanish system gained the most improvement from Wiktionary pronunciations with 7.22% relative word error rate reduction.
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paper_title: A Morpho-graphemic Approach for the Recognition of Spontaneous Speech in Agglutinative Languages - like Hungarian

paper_content:

A coupled acoustic- and language-modeling approach is presented for the recognition of spontaneous speech primarily in agglutinative languages. The effectiveness of the approach in large vocabulary spontaneous speech recognition is demonstrated on the Hungarian MALACH corpus. The derivation of morphs from word forms is based on a statistical morphological segmentation tool while the mapping of morphs into graphemes is obtained trivially by splitting each morph into individual letters. Using morphs instead of words in language modeling gives significant WER reductions in case of both phoneme- and grapheme-based acoustic modeling. The improvements are larger after speaker adaptation of the acoustic models. In conclusion, morphophonemic and the proposed morpho-graphemic ASR approaches yield the same best WERs, which are significantly lower than the word-based baselines but essentially without language dependent rules or pronunciation dictionaries in the latter case. Index Terms: spontaneous speech recognition, morphology.
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paper_title: Morphology-based language modeling for Arabic speech recognition

paper_content:

Abstract : Language modeling is a difficult problem for languages with rich morphology. In this paper we investigate the use of morphology-based language models at different stages in a speech recognition system for conversational Arabic. Class-based and single-stream factored language models using morphological word representations are applied within an N-best list rescoring framework. In addition, we explore the use of factored language models in first-pass recognition, which is facilitated by two novel procedures: the data-driven optimization of a multi-stream language model structure, and the conversion of a factored language model to a standard word-based model. We evaluate these techniques on a large-vocabulary recognition task and demonstrate that they lead to perplexity and word error rate reductions.
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paper_title: Turkish LVCSR: towards better speech recognition for agglutinative languages

paper_content:

The Turkish language belongs to the Turkic family. All members of this family are close to one another in terms of linguistic structure. Typological similarities are vowel harmony, verb-final word order and agglutinative morphology. This latter property causes a very fast vocabulary growth resulting in a large number of out-of-vocabulary words. In this paper we describe our first experiments in a speaker independent LVCSR engine for Modern Standard Turkish. First results on our Turkish speech recognition system are presented. The currently best system shows very promising results achieving 16.9% word error rate. To overcome the OOV-problem we propose a morphem-based and the Hypothesis Driven Lexical Adaptation approach. The final Turkish system is integrated into the multilingual recognition engine of the GlobalPhone project.
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paper_title: Joint Morphological-Lexical Language Modeling (JMLLM) for Arabic

paper_content:

Language modeling for inflected languages such as Arabic poses new challenges for speech recognition due to rich morphology. The rich morphology results in large increases in perplexity and out-of-vocabulary (OOV) rate. In this study, we present a new language modeling method that takes advantage of Arabic morphology by combining morphological segments with the underlying lexical items and additional available information sources with regards to morphological segments and lexical items within a single joint model. Joint representation and modeling of morphological and lexical items reduces the OOV rate and provides smooth probability estimates. Preliminary experiments detailed in this paper show satisfactory improvements over word and morpheme based trigram language models and their interpolations.
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paper_title: Unsupervised segmentation of words into morphemes – Morpho Challenge 2005: Application to automatic speech recognition

paper_content:

Within the EU Network of Excellence PASCAL, a challenge was organized to design a statistical machine learning algorithm that segments words into the smallest meaning-bearing units of language, morphemes. Ideally, these are basic vocabulary units suitable for different tasks, such as speech and text understanding, machine translation, information retrieval, and statistical language modeling. Twelve research groups participated in the challenge and had submitted segmentation results obtained by their algorithms. In this paper, we evaluate the application of these segmentation algorithms to large vocabulary speech recognition using statistical n-gram language models based on the proposed word segments instead of entire words. Experiments were done for two agglutinative and morphologically rich languages: Finnish and Turkish. We also investigate combining various segmentations to improve the performance of the recognizer. Index Terms: speech recognition, language modelling, morphemes, unsupervised learning.
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paper_title: Large vocabulary continuous speech recognition of an inflected language using stems and endings

paper_content:

In this article, we focus on creating a large vocabulary speech recognition system for the Slovenian language. Currently, state-of-the-art recognition systems are able to use vocabularies with sizes of 20,000 to 100,000 words. These systems have mostly been developed for English, which belongs to a group of uninflectional languages. Slovenian, as a Slavic language, belongs to a group of inflectional languages. Its rich morphology presents a major problem in large vocabulary speech recognition. Compared to English, the Slovenian language requires a vocabulary approximately 10 times greater for the same degree of text coverage. Consequently, the difference in vocabulary size causes a high degree of OOV (out-of-vocabulary words). Therefore OOV words have a direct impact on recognizer efficiency. The characteristics of inflectional languages have been considered when developing a new search algorithm with a method for restricting the correct order of sub-word units, and to use separate language models based on sub-words. This search algorithm combines the properties of sub-word-based models (reduced OOV) and word-based models (the length of context). The algorithm also enables better search-space limitation for sub-word models. Using sub-word models, we increase recognizer accuracy and achieve a comparable search space to that of a standard word-based recognizer. Our methods were evaluated in experiments on a SNABI speech database.
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paper_title: Cross-language bootstrapping for unsupervised acoustic model training: Rapid development of a polish speech recognition system

paper_content:

This paper describes the rapid development of a Polish language speech recognition system. The system development was performed without access to any transcribed acoustic training data. This was achieved through the combined use of cross-language bootstrapping and confidence based unsupervised acoustic model training. A Spanish acoustic model was ported to Polish, through the use of a manually constructed phoneme mapping. This initial model was refined through iterative recognition and retraining of the untranscribed audio data. The system was trained and evaluated on recordings from the European Parliament, and included several state-of-the-art speech recognition techniques in addition to the use of unsupervised model training. Confidence based speaker adaptive training using features space transform adaptation, as well as vocal tract length normalization and maximum likelihood linear regression, was used to refine the acoustic model. Through the combination of the different techniques, good performance was achieved on the domain of parliamentary speeches. Index Terms: speech recognition, unsupervised training, crosslanguage bootstrapping
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paper_title: Uyghur morpheme-based language models and ASR

paper_content:

Uyghur language is an agglutinative language in which words are formed by suffixes attaching to a stem (or root). Because of the explosive nature in vocabulary of the agglutinative languages, several morpheme-based language models are built and experiments are implemented. Morpheme is the smallest meaning bearing unit. In this research, morpheme is referred to any of prefix, stem, or suffix. As a result, a large vocabulary ASR system is built on the basis of Julius system. Several ASR results on language models based on different units (word, morpheme, and syllable) are compared.
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paper_title: Exploiting Syntactic Structure for Language Modeling

paper_content:

The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words-binary-parse-structure with headword annotation and operates in a left-to-right manner --- therefore usable for automatic speech recognition. The model, its probabilistic parameterization, and a set of experiments meant to evaluate its predictive power are presented; an improvement over standard trigram modeling is achieved.
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paper_title: Using different acoustic, lexical and language modeling units for ASR of an under-resourced language - Amharic

paper_content:

State-of-the-art large vocabulary continuous speech recognition systems use mostly phone based acoustic models (AMs) and word based lexical and language models. However, phone based AMs are not efficient in modeling long-term temporal dependencies and the use of words in lexical and language models leads to out-of-vocabulary (OOV) problem, which is a serious issue for morphologically rich languages. This paper presents the results of our contributions on the use of different units for acoustic, lexical and language modeling for an under-resourced language (Amharic spoken in Ethiopia). Triphone, Syllable and hybrid (syllable-phone) units have been investigated for acoustic modeling. Word and morphemes have been investigated for lexical and language modeling. We have also investigated the use of longer (syllable) acoustic units and shorter (morpheme) lexical as well as language modeling units in a speech recognition system. Although hybrid AMs did not bring much improvement over context dependent syllable based recognizers in speech recognition performance with word based lexical and language model (i.e. word based speech recognition), we observed a significant word error rate (WER) reduction compared to triphone-based systems in morpheme-based speech recognition. Syllable AMs also led to a WER reduction over the triphone-based systems both in word based and morpheme based speech recognition. It was possible to obtain a 3% absolute WER reduction as a result of using syllable acoustic units in morpheme-based speech recognition. Overall, our result shows that syllable and hybrid AMs are best fitted in morpheme-based speech recognition.
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paper_title: . Localization of Speech Recognition in Spoken Dialog Systems: How Machine Translation Can Make Our Lives Easier .

paper_content:

The localization of speech recognition for large-scale spoken dialog systems can be a tremendous exercise. Usually, all in- volved grammars have to be translated by a language expert, and new data has to be collected, transcribed, and annotated for statistical utterance classifiers resulting in a time-consuming and expensive undertaking. Often though, a vast number of transcribed and annotated utterances exists for the source lan- guage. In this paper, we propose to use such data and translate it into the target language using machine translation. The trans- lated utterances and their associated (original) annotations are then used to train statistical grammars for all contexts of the target system. As an example, we localize an English spoken dialog system for Internet troubleshooting to Spanish by trans- lating more than 4 million source utterances without any human intervention. In an application of the localized system to more than 10,000 utterances collected on a similar Spanish Internet troubleshooting system, we show that the overall accuracy was only 5.7% worse than that of the English source system. Index Terms: spoken dialog systems, machine translation, lo- calization
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paper_title: Morph-based speech recognition and modeling of out-of-vocabulary words across languages

paper_content:

We explore the use of morph-based language models in large-vocabulary continuous-speech recognition systems across four so-called morphologically rich languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. The morphs are subword units discovered in an unsupervised, data-driven way using the Morfessor algorithm. By estimating n-gram language models over sequences of morphs instead of words, the quality of the language model is improved through better vocabulary coverage and reduced data sparsity. Standard word models suffer from high out-of-vocabulary (OOV) rates, whereas the morph models can recognize previously unseen word forms by concatenating morphs. It is shown that the morph models do perform fairly well on OOVs without compromising the recognition accuracy on in-vocabulary words. The Arabic experiment constitutes the only exception since here the standard word model outperforms the morph model. Differences in the datasets and the amount of data are discussed as a plausible explanation.
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paper_title: Morphological random forests for language modeling of inflectional languages

paper_content:

In this paper, we are concerned with using decision trees (DT) and random forests (RF) in language modeling for Czech LVCSR. We show that the RF approach can be successfully implemented for language modeling of an inflectional language. Performance of word-based and morphological DTs and RFs was evaluated on lecture recognition task. We show that while DTs perform worse than conventional trigram language models (LM), RFs of both kind outperform the latter. WER (up to 3.4% relative) and perplexity (10%) reduction over the trigram model can be gained with morphological RFs. Further improvement is obtained after interpolation of DT and RF LMs with the trigram one (up to 15.6% perplexity and 4.8% WER relative reduction). In this paper we also investigate distribution of morphological feature types chosen for splitting data at different levels of DTs.
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paper_title: A new ASR evaluation measure and minimum Bayes-risk decoding for open-domain speech understanding

paper_content:

A new evaluation measure of speech recognition and a decoding strategy for keyword-based open-domain speech understanding are presented. Conventionally, WER (word error rate) has been widely used as an evaluation measure of speech recognition, which treats all words in a uniform manner. We define a weighted keyword error rate (WKER) which gives a weight on errors from a viewpoint of information retrieval. We first demonstrate that this measure is more appropriate for predicting the performance of key sentence indexing of oral presentations. Then, we formulate a decoding method to minimize WKER based on a minimum Bayes-risk (MBR) framework, and show that the decoding method works reasonably for improving WKER and key sentence indexing.
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paper_title: SMT-based ASR domain adaptation methods for under-resourced languages: Application to Romanian

paper_content:

This study investigates the possibility of using statistical machine translation to create domain-specific language resources. We propose a methodology that aims to create a domain-specific automatic speech recognition (ASR) system for a low-resourced language when in-domain text corpora are available only in a high-resourced language. Several translation scenarios (both unsupervised and semi-supervised) are used to obtain domain-specific textual data. Moreover this paper shows that a small amount of manually post-edited text is enough to develop other natural language processing systems that, in turn, can be used to automatically improve the machine translated text, leading to a significant boost in ASR performance. An in-depth analysis, to explain why and how the machine translated text improves the performance of the domain-specific ASR, is also made at the end of this paper. As bi-products of this core domain-adaptation methodology, this paper also presents the first large vocabulary continuous speech recognition system for Romanian, and introduces a diacritics restoration module to process the Romanian text corpora, as well as an automatic phonetization module needed to extend the Romanian pronunciation dictionary.
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paper_title: . Localization of Speech Recognition in Spoken Dialog Systems: How Machine Translation Can Make Our Lives Easier .

paper_content:

The localization of speech recognition for large-scale spoken dialog systems can be a tremendous exercise. Usually, all in- volved grammars have to be translated by a language expert, and new data has to be collected, transcribed, and annotated for statistical utterance classifiers resulting in a time-consuming and expensive undertaking. Often though, a vast number of transcribed and annotated utterances exists for the source lan- guage. In this paper, we propose to use such data and translate it into the target language using machine translation. The trans- lated utterances and their associated (original) annotations are then used to train statistical grammars for all contexts of the target system. As an example, we localize an English spoken dialog system for Internet troubleshooting to Spanish by trans- lating more than 4 million source utterances without any human intervention. In an application of the localized system to more than 10,000 utterances collected on a similar Spanish Internet troubleshooting system, we show that the overall accuracy was only 5.7% worse than that of the English source system. Index Terms: spoken dialog systems, machine translation, lo- calization
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paper_title: ASR domain adaptation methods for low-resourced languages: Application to Romanian language

paper_content:

This study investigates the possibility of using statistical machine translation to create domain-specific language resources. We propose a methodology that aims to create a domain-specific automatic speech recognition system for a low-resourced language when in-domain text corpora are available only in a high-resourced language. We evaluate a new semi-supervised method and compare it with previously developed semi-supervised and unsupervised approaches. Moreover, in the effort of creating an out-of-domain language model for Romanian, we introduce and experiment an effective diacritics restoration algorithm.
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paper_title: Using the Web for fast language model construction in minority languages

paper_content:

The design and construction of a language model for minority languages is a hard task. By minority language, we mean a language with small available resources, especially for the statistical learning problem. In this paper, a new methodology for fast language model construction in minority languages is proposed. It is based on the use of Web resources to collect and make efficient textual corpora. By using some filtering techniques, this methodology allows a quick and efficient construction of a language model with a small cost in term of computational and human resources. Our primary experiments have shown excellent performance of the Web language models vs newspaper language models using the proposed filtering methods on a majority language (French). Following the same way for a minority language (Vietnamese), a valuable language model was constructed in 3 month with only 15% new development to modify some filtering tools.
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paper_title: Transcribing Southern Min speech corpora with a Web-Based language learning system

paper_content:

The paper proposes a human-computation-based scheme for transcribing Southern Min speech corpora. The core idea is to implement a Web-based language learning system to collect orthographic and phonetic labels from a large amount of language learners and choose the commonly input labels as the transcriptions of the corpora. It is essentially a technology of distributed knowledge acquisition. Some computer-aided mechanisms are also used to verify the collected transcriptions. The benefit of the scheme is that it makes the transcribing task neither tedious nor costly. No significant budget should be made for transcribing large corpora. The design of a system for transcribing Min Nan speech corpora is described in detail. The application of a prototype version of the system shows that this transcribing scheme is an effective and economical way to generate orthographic and phonetic transcriptions.
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paper_title: A new ASR evaluation measure and minimum Bayes-risk decoding for open-domain speech understanding

paper_content:

A new evaluation measure of speech recognition and a decoding strategy for keyword-based open-domain speech understanding are presented. Conventionally, WER (word error rate) has been widely used as an evaluation measure of speech recognition, which treats all words in a uniform manner. We define a weighted keyword error rate (WKER) which gives a weight on errors from a viewpoint of information retrieval. We first demonstrate that this measure is more appropriate for predicting the performance of key sentence indexing of oral presentations. Then, we formulate a decoding method to minimize WKER based on a minimum Bayes-risk (MBR) framework, and show that the decoding method works reasonably for improving WKER and key sentence indexing.
---
paper_title: Uyghur morpheme-based language models and ASR

paper_content:

Uyghur language is an agglutinative language in which words are formed by suffixes attaching to a stem (or root). Because of the explosive nature in vocabulary of the agglutinative languages, several morpheme-based language models are built and experiments are implemented. Morpheme is the smallest meaning bearing unit. In this research, morpheme is referred to any of prefix, stem, or suffix. As a result, a large vocabulary ASR system is built on the basis of Julius system. Several ASR results on language models based on different units (word, morpheme, and syllable) are compared.
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paper_title: Accent Modeling Based On Pronunciation Dictionary Adaptation For Large Vocabulary Mandarin Speech Recognition

paper_content:

A method of accent modeling through Pronunciation Dictionary Adaptation (PDA) is presented. We derive the pronunciation variation between canonical speaker groups and accent groups and add an encoding of the differences to a canonical dictionary to create a new, adapted dictionary that reflects the accent characteristics. The pronunciation variation information is then integrated with acoustic and language models into a one-pass search framework. It is assumed that acoustic deviation and pronunciation variation are independent but complementary phenomena that cause poor performance among accented speakers. Therefore, MLLR, an efficient model adaptation technique, is also presented both alone and in combination with PDA. It is shown that when PDA, MLLR and PDA+MLLR are used, error rate reductions of 13.9%, 24.1% and 28.4% respectively are achieved.
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paper_title: Avaaj Otalo: a field study of an interactive voice forum for small farmers in rural India

paper_content:

In this paper we present the results of a field study of Avaaj Otalo (literally, "voice stoop"), an interactive voice application for small-scale farmers in Gujarat, India. Through usage data and interviews, we describe how 51 farmers used the system over a seven month pilot deployment. The most popular feature of Avaaj Otalo was a forum for asking questions and browsing others' questions and responses on a range of agricultural topics. The forum developed into a lively social space with the emergence of norms, persistent moderation, and a desire for both structured interaction with institutionally sanctioned authorities and open discussion with peers. For all 51 users this was the first experience participating in an online community of any sort. In terms of usability, simple menu-based navigation was readily learned, with users preferring numeric input over speech. We conclude by discussing implications of our findings for designing voice-based social media serving rural communities in India and elsewhere.
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paper_title: Integrating Thai grapheme based acoustic models into the ML-MIX framework - For language independent and cross-language ASR

paper_content:

Grapheme based speech recognition is a powerful tool for rapidly creating automatic speech recognition (ASR) systems in new languages. For purposes of language independent or cross language speech recognition it is necessary to identify similar models in the different languages involved. For phoneme based multilingual ASR systems this is usually achieved with the help of a language independent phoneme set and the corresponding phoneme identities in the different languages. For grapheme based multilingual ASR systems this is only possible when there is an overlap in graphemes of the different scripts involved. Often this is not the case, as for example for Thai which graphemes does not have any overlap with the graphemes of the languages that we used for multilingual grapheme based ASR in the past. In order to be able to apply our multilingual grapheme model to Thai, and in order to incorporate Thai into our multilingual recognizer, we examined and evaluated a number of data driven distance measures between the multilingual grapheme models. For our purposes distance measures that rely directly on the parameters of the models, such as the Kullback-Leibler and the Bhatthacharya distance yield the best performance.
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paper_title: Word segmentation through cross-lingual word-to-phoneme alignment

paper_content:

We present our new alignment model Model 3P for cross-lingual word-to-phoneme alignment, and show that unsupervised learning of word segmentation is more accurate when information of another language is used. Word segmentation with cross-lingual information is highly relevant to bootstrap pronunciation dictionaries from audio data for Automatic Speech Recognition, bypass the written form in Speech-to-Speech Translation or build the vocabulary of an unseen language, particularly in the context of under-resourced languages. Using Model 3P for the alignment between English words and Spanish phonemes outperforms a state-of-the-art monolingual word segmentation approach [1] on the BTEC corpus [2] by up to 42% absolute in F-Score on the phoneme level and a GIZA++ alignment based on IBM Model 3 by up to 17%.
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paper_title: Rapid building of an ASR system for Under-Resourced Languages based on Multilingual Unsupervised Training

paper_content:

This paper presents our work on rapid language adaptation of acoustic models based on multilingual cross-language bootstrapping and unsupervised training. We used Automatic Speech Recognition (ASR) systems in the six source languages English, French, German, Spanish, Bulgarian and Polish to build from scratch an ASR system for Vietnamese, an underresourced language. System building was performed without using any transcribed audio data by applying three consecutive steps, i.e. cross-language transfer, unsupervised training based on the “multilingual A-stabil” confidence score [1], and bootstrapping. We investigated the correlation between performance of “multilingual A-stabil” and the number of source languages and improved the performance of “multilingual A-stabil” by applying it at the syllable level. Furthermore, we showed that increasing the amount of source language ASR systems for the multilingual framework results in better performance of the final ASR system in the target language Vietnamese. The final Vietnamese recognition system has a Syllable Error Rate (SyllER) of 16.8% on the development set and 16.1% on the evaluation set. Index Terms: rapid language adaptation of ASR, unsupervised training, multilingual A-Stabil
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paper_title: Multilingual a-stabil: A new confidence score for multilingual unsupervised training

paper_content:

This paper presents our work in Automatic Speech Recognition (ASR) in the context of multilingual unsupervised training with application to Czech. Starting without any transcribed acoustic training data we built a Czech ASR by combining cross-language bootstrapping and confidence based unsupervised training. We present our new method called “multilingual A-stabil” to compute confidence scores and explore the relative effectiveness of acoustic models from more than one language such as Russian, Bulgarian, Polish and Croatian for unsupervised training. While conventional confidence measures such as gamma and A-stabil [1] [2] work well with well-trained acoustic models but have problems with poorly estimated acoustic models, our new method works well in both cases. We describe our multilingual unsupervised training framework which gives very promising results in our experiments. We were able to select 80.5% of the audio training data (18.5 hours) with a transcription WER of 14.5% when using a small amount of untranscribed data (only about 23 hours). The final best WER on Czech is 23.6% on the development set and 22.9% on the evaluation set by using cross-lingual boostrapping, which is very close to the performance of the Czech ASR trained with 23 hours audio data with manual transcriptions (23.1% on the development set and 22.3% on the evaluation set).
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paper_title: Woefzela - an open-source platform for ASR data collection in the developing world

paper_content:

This project was made possible through the support of the South ::: African National Centre for Human Language Technology, an ::: initiative of the South African Department of Arts and Culture. ::: The authors would also like to thank Pedro Moreno, Thad ::: Hughes and Ravindran Rajakumar of Google Research for valuable ::: inputs at various stages of this work.
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paper_title: Uyghur morpheme-based language models and ASR

paper_content:

Uyghur language is an agglutinative language in which words are formed by suffixes attaching to a stem (or root). Because of the explosive nature in vocabulary of the agglutinative languages, several morpheme-based language models are built and experiments are implemented. Morpheme is the smallest meaning bearing unit. In this research, morpheme is referred to any of prefix, stem, or suffix. As a result, a large vocabulary ASR system is built on the basis of Julius system. Several ASR results on language models based on different units (word, morpheme, and syllable) are compared.
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Title: Automatic Speech Recognition for Under-Resourced Languages: A Survey

Section 1: Introduction
Description 1: This section focuses on the language diversity and the motivation to address the topic of automatic speech recognition for under-resourced languages.

Section 2: Under-Resourced (UR) Languages: Definition and Challenges
Description 2: This section provides a definition of what constitutes under-resourced languages and discusses the various challenges associated with them.

Section 3: Literature Review
Description 3: This section reviews the recent contributions and developments in the field of automatic speech recognition for under-resourced languages.

Section 4: Past Projects on U-ASR
Description 4: This section gives examples of past projects that have worked on automatic speech recognition for under-resourced languages, detailing their approaches and outcomes.

Section 5: Future Trends
Description 5: This section presents the future trends and directions in the field when dealing with under-resourced languages.

Section 6: Conclusion
Description 6: This section summarizes the findings of the survey, highlighting the key points and the progress made so far. It also discusses the importance of continued efforts in speech recognition for under-resourced languages.