NDLI: Deep bi-directional recurrent networks over spectral windows

Content Provider	IEEE Xplore Digital Library
Author	Mohamed, Abdel-rahman Seide, Frank Yu, Dong Droppo, Jasha Stoicke, Andreas Zweig, Geoffrey Penn, Gerald
Copyright Year	2015
Description	Author affiliation: University of Toronto (Penn, Gerald) \|\| Microsoft Research (Mohamed, Abdel-rahman; Seide, Frank; Yu, Dong; Droppo, Jasha; Stoicke, Andreas; Zweig, Geoffrey)
Abstract	Long short-term memory (LSTM) acoustic models have recently achieved state-of-the-art results on speech recognition tasks. As a type of recurrent neural network, LSTMs potentially have the ability to model long-span phenomena relating the spectral input to linguistic units. However, it has not been clear whether their observed performance is actually due to this capability, or instead if it is due to a better modeling of short term dynamics through the recurrence. In this paper. we answer this question by applying a windowed (truncated) LSTM to conversational speech transcription, and find that a limited context is adequate, and that it is not necessaary to scan the entire utterance. The sliding window approach allows not only incremental (online) recognition with a bidirectional model, but also frame-wise randomization (as opposed to utterance randomization), which results in faster convergence. On the SWBD/Fisher corpus, applying bidirectional LSTM RNNs to spectral windows of about 0.5s improves WER on the Hub5'00 benchmark set by 16% relative compared to our best sequence-trained DNN. On an extended 3850h training set that that also includes lectures, the relative gain becomes 28% (Hub5'00 WER 9.2%). In-house conversational data improves by 12 to 17% relative.
Starting Page	78
Ending Page	83
File Size	451895
Page Count	6
File Format	PDF
e-ISBN	9781479972913
DOI	10.1109/ASRU.2015.7404777
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Acoustics Training Bidirectional control Speech Recurrent neural networks Context acoustic modeling Recurrent networks LSTM Deep learning
Content Type	Text
Resource Type	Article

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