IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Special Section on Recent Advances in Machine Learning for Spoken Language Processing
Investigation of DNN-Based Audio-Visual Speech Recognition
Satoshi TAMURAHiroshi NINOMIYANorihide KITAOKAShin OSUGAYurie IRIBEKazuya TAKEDASatoru HAYAMIZU
Author information
JOURNALS FREE ACCESS

2016 Volume E99.D Issue 10 Pages 2444-2451

Details
Abstract

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.

Information related to the author
© 2016 The Institute of Electronics, Information and Communication Engineers
Previous article Next article
feedback
Top