End-to-End Open Vocabulary Keyword Search With Multilingual Neural
  Representations
Bolaji Yusuf Jan Cernocky Murat Saraclar
Conventional keyword search systems operate on automatic speech recognition
(ASR) outputs, which causes them to have a complex indexing and search
pipeline. This has led to interest in ASR-free approaches to simplify the
search procedure. We recently proposed a neural ASR-free keyword search model
which achieves competitive performance while maintaining an efficient and
simplified pipeline, where queries and documents are encoded with a pair of
recurrent neural network encoders and the encodings are combined with a
dot-product. In this article, we extend this work with multilingual pretraining
and detailed analysis of the model. Our experiments show that the proposed
multilingual training significantly improves the model performance and that
despite not matching a strong ASR-based conventional keyword search system for
short queries and queries comprising in-vocabulary words, the proposed model
outperforms the ASR-based system for long queries and queries that do not
appear in the training data.