A robust audio-visual speech recognition using Audio-Visual Voice Activity Detection

This paper proposes a novel speech recognition method combining Audio-Visual Voice Activity Detection (AVVAD) and Audio-Visual Automatic Speech Recognition (AVASR). AVASR has been developed to enhance the robustness of ASR in noisy environments, using visual information in addition to acoustic features. Similarly, AVVAD increases the precision of VAD in noisy conditions, which detects presence of speech from an audio signal. In our approach, AVVAD is conducted as a preprocessing followed by an AVASR system, making a significantly robust speech recognizer. To evaluate the proposed system, recognition experiments were conducted using noisy audio-visual data, testing several AVVAD approaches. Then it is found that the proposed AVASR system using the model-free feature-fusion AVVAD method outperforms not only non-VAD audio-only ASR but also conventional AVASR.