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arXiv · 1705.05992

Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model

Xu Tian·Jun Zhang·Zejun Ma·Yi He·Juan Wei
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Abstract

Frame stacking is broadly applied in end-to-end neural network training like connectionist temporal classification (CTC), and it leads to more accurate models and faster decoding. However, it is not well-suited to conventional neural network based on context-dependent state acoustic model, if the decoder is unchanged. In this paper, we propose a novel frame retaining method which is applied in decoding. The system which combined frame retaining with frame stacking could reduces the time consumption of both training and decoding. Long short-term memory (LSTM) recurrent neural networks (RNNs) using it achieve almost linear training speedup and reduces relative 41\% real time factor (RTF). At the same time, recognition performance is no degradation or improves sightly on Shenma voice search dataset in Mandarin.

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Xu Tian, Jun Zhang, Zejun Ma, Yi He, Juan Wei. 2017-05-17. Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model. https://arxiv.org/abs/1705.05992

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