Temporal Asynchronicity Modeling by Product HMMS for Audio-Visual Speech Recognition
Abstract
There have been higher demands recently for Automatic Speech Recognition (ASR) systems able to operate robustly in acoustically noisy environments. This paper proposes a method to effectively integrate audio and visual information in audio-visual (bi-modal) ASR systems. Such integration inevitably necessitates modeling of the synchronization and asynchronization of the audio and visual information. To address the time lag and correlation problems in individual features between speech and lip movements, we introduce a type of integrated HMM modeling of audio-visual information based on a family of a product HMM. The proposed model can represent state synchronicity not only within a phoneme but also between phonemes. Furthermore, we also propose a rapid stream weight optimization based on GPD algorithm for noisy bi-modal speech recognition. Evaluation experiments show that the proposed method improves the recognition accuracy for noisy speech. in SNR=0db our proposed method attained 16% higher performance compared to a product HMMs without the synchronicity re-estimation.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 12, 2002
- Accession Number
- ADP014022
Entities
People
- Satoshi Nakamura