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arXiv · 1612.04675

Recurrent Deep Stacking Networks for Speech Recognition

Peidong Wang·Zhongqiu Wang·Deliang Wang
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Abstract

This paper presented our work on applying Recurrent Deep Stacking Networks (RDSNs) to Robust Automatic Speech Recognition (ASR) tasks. In the paper, we also proposed a more efficient yet comparable substitute to RDSN, Bi- Pass Stacking Network (BPSN). The main idea of these two models is to add phoneme-level information into acoustic models, transforming an acoustic model to the combination of an acoustic model and a phoneme-level N-gram model. Experiments showed that RDSN and BPsn can substantially improve the performances over conventional DNNs.

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Peidong Wang, Zhongqiu Wang, Deliang Wang. 2016-12-14. Recurrent Deep Stacking Networks for Speech Recognition. https://arxiv.org/abs/1612.04675

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