A Comparison of Adaptation Techniques and Recurrent Neural Network Architectures

Recently, recurrent neural networks have become state-of-the-art in acoustic modeling for automatic speech recognition. The long short-term memory (LSTM) units are the most popular ones. However, alternative units like gated recurrent unit (GRU) and its modifications outperformed LSTM in some public...

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Bibliographic Details
Published inarXiv.org
Main Authors Vanek, Jan, Michalek, Josef, Zelinka, Jan, Psutka, Josef
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.07.2018
Subjects
Adaptation
Automatic speech recognition
Neural networks
Recurrent neural networks
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Summary:Recently, recurrent neural networks have become state-of-the-art in acoustic modeling for automatic speech recognition. The long short-term memory (LSTM) units are the most popular ones. However, alternative units like gated recurrent unit (GRU) and its modifications outperformed LSTM in some publications. In this paper, we compared five neural network (NN) architectures with various adaptation and feature normalization techniques. We have evaluated feature-space maximum likelihood linear regression, five variants of i-vector adaptation and two variants of cepstral mean normalization. The most adaptation and normalization techniques were developed for feed-forward NNs and, according to results in this paper, not all of them worked also with RNNs. For experiments, we have chosen a well known and available TIMIT phone recognition task. The phone recognition is much more sensitive to the quality of AM than large vocabulary task with a complex language model. Also, we published the open-source scripts to easily replicate the results and to help continue the development.
ISSN:2331-8422