Investigation of DNN-Based Audio-Visual Speech Recognition

Satoshi TAMURA  Hiroshi NINOMIYA  Norihide KITAOKA  Shin OSUGA  Yurie IRIBE  Kazuya TAKEDA  Satoru HAYAMIZU  

IEICE TRANSACTIONS on Information and Systems   Vol.E99-D   No.10   pp.2444-2451
Publication Date: 2016/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016SLP0019
Type of Manuscript: Special Section PAPER (Special Section on Recent Advances in Machine Learning for Spoken Language Processing)
Category: Acoustic modeling
audio-visual speech recognition,  deep neural network,  Deep Bottleneck Feature,  multi-stream HMM,  

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Audio-Visual Speech Recognition (AVSR) is one of techniques to enhance robustness of speech recognizer in noisy or real environments. On the other hand, Deep Neural Networks (DNNs) have recently attracted a lot of attentions of researchers in the speech recognition field, because we can drastically improve recognition performance by using DNNs. There are two ways to employ DNN techniques for speech recognition: a hybrid approach and a tandem approach; in the hybrid approach an emission probability on each Hidden Markov Model (HMM) state is computed using a DNN, while in the tandem approach a DNN is composed into a feature extraction scheme. In this paper, we investigate and compare several DNN-based AVSR methods to mainly clarify how we should incorporate audio and visual modalities using DNNs. We carried out recognition experiments using a corpus CENSREC-1-AV, and we discuss the results to find out the best DNN-based AVSR modeling. Then it turns out that a tandem-based method using audio Deep Bottle-Neck Features (DBNFs) and visual ones with multi-stream HMMs is the most suitable, followed by a hybrid approach and another tandem scheme using audio-visual DBNFs.