A Comprehensive Survey on Bengali Phoneme Recognition
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paper_title: A contrastive analysis of English and Bangla phonemics

paper_content:

Contrastive phonemics is the field of study in which different phonemic systems are laid side by side to find out similarities and dissimilarities between the phonemes of the languages concerned. Every language has its own phonemic system, which holds unique as well as common features. A language shares some phonemes with other languages, but no two languages have the same phonemic inventory. This article makes a contrastive analysis of the phonemic systems of English and Bangla. The aspects of similarities as well as dissimilarities between the two have been explored in detail. It brings into focus the inventory of phonemes of the two languages along with relevant phonetic and phonological characteristics. The vowel and consonant phonemes of the two languages have been compared with sufficient examples, making it clear where and how they are identical and different. Key words: contrastive; Bangla; English; phonemics DOI: 10.3329/dujl.v2i4.6898 Dhaka University Journal of Linguistics Vol.2(4) August 2009 pp.19-42
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paper_title: The Indo-Aryan Languages

paper_content:

1. Introduction 2. The modern Indo-Aryan languages and dialects 3. The historical context and development of Indo-Aryan 4. The nature of the New Indo-Aryan lexicon 5. NIA descriptive phonology 6. Writing systems 7. Historical phonology 8. Nominal forms and categories 9. Verbal forms and categories 10. Syntax Appendix I Inventory of NIA languages and dialects Appendix II Schemes of NIA subclassification.
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paper_title: Bangla phoneme recognition for ASR using multilayer neural network

paper_content:

This paper presents a Bangla phoneme recognition method for Automatic Speech Recognition (ASR). The method consists of two stages: i) a multilayer neural network (MLN), which converts acoustic features, mel frequency cepstral coefficients (MFCCs), into phoneme probabilities and ii) the phoneme probabilities obtained from the first stage and corresponding Δ and ΔΔ parameters calculated by linear regression (LR) are inserted into a hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. From the experiments on Bangla speech corpus prepared by us, it is observed that the proposed method provides higher phoneme recognition performance than the existing method. Moreover, it requires a fewer mixture components in the HMMs.
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paper_title: Phonetic Features enhancement for Bangla automatic speech recognition

paper_content:

This paper discusses a phonetic feature (PF) based automatic speech recognition system (ASR) for Bangla (widely known as Bengali), where the PF features are enhanced. There are three stages in this method where the first step maps Acoustic Features (AFs) or Local Features (LFs) into Phonetic Features (PFs) and the second step incorporates inhibition/enhancement (In/En) algorithm to change the PF dynamic patterns where patterns are enhanced for convex patterns and inhibited for concave patterns. The final step is for normalizing the extended PF vector using Gram-Schmidt algorithm and then passing through a Hidden Markov Model (HMM) based classifier. In our experiment on speech corpus for Bangla, the proposed feature extraction method provides higher sentence correct rate (SCR), word correct rate (WCR) and word accuracy (WA) compared to the methods that not incorporated In/En network.
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Title: A Comprehensive Survey on Bengali Phoneme Recognition

Section 1: INTRODUCTION
Description 1: This section provides an overview of phonemes, the importance of Bengali language, and the aim of the survey.

Section 2: Bangla phoneme
Description 2: This section describes the phonetic inventory of the Bengali language, including the number of vowels and consonants.

Section 3: Scope of Automatic Speech Recognition or Phoneme Recognition
Description 3: This section discusses the relevance of phoneme recognition in ASR systems and the challenges associated with Bengali language.

Section 4: PHONEME RECOGNITION METHODS
Description 4: This section details various methods used for phoneme recognition, including hidden Markov models and their limitations in real acoustic conditions.

Section 5: Multilayer neural network
Description 5: This section explains the use of artificial neural networks, specifically multilayer neural networks, for Bengali phoneme recognition.

Section 6: Phonetic Feature Table
Description 6: This section describes the phonetic features used to identify Bengali phonemes and the specifics of their representation.

Section 7: Phonetic Features Enhancement
Description 7: This section covers advanced methods for phonetic feature extraction and enhancement, incorporating multilayer neural networks and other techniques.

Section 8: RESULT AND ANALYSIS
Description 8: This section presents the results from various phoneme recognition methods discussed in the paper, comparing their performance.

Section 9: CONCLUSION
Description 9: This section summarizes the survey, highlighting the advantages of different phoneme recognition methods and provides a brief conclusion.