Dynamic Bayesian Networks for Audio-Visual Speech Recognition

作者：Ara Nefian, Luhong Liang, Xiaobo Pi, Xiaoxing Liu, Kevin J. Murphy · 发表于：EURASIP Journal on Advances in Signal Processing · 年份：2002 · DOI：10.1155/s1110865702206083 · 被引用次数：321 · 研究领域：Speech and Audio Processing、Music and Audio Processing、Blind Source Separation Techniques

The use of visual features in audio-visual speech recognition (AVSR) is justified by both the speech generation mechanism, which is essentially bimodal in audio and visual representation, and by the need for features that are invariant to acoustic noise perturbation. As a result, current AVSR systems demonstrate significant accuracy improvements in environments affected by acoustic noise. In this paper, we describe the use of two statistical models for audio-visual integration, the coupled HMM (CHMM) and the factorial HMM (FHMM), and compare the performance of these models with the existing models used in speaker dependent audio-visual isolated word recognition. The statistical properties of both the CHMM and FHMM allow to model the state asynchrony of the audio and visual observation sequences while preserving their natural correlation over time. In our experiments, the CHMM performs best overall, outperforming all the existing models and the FHMM.