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One of the main challenges in speech recognition is developing systems that are robust to contamination by intrusive background noise. In audio-visual speech recognition (AVSR), audio information is augmented by visual information in order to help improve the performance of speech recognition, particularly when the audio modality is so significantly corrupted by background noise and it becomes hard to differentiate the original speech signal from the noise. The signal-to-noise ratio (SNR) can be used to identify the level of noise in original speech signal and one widely used method for SNR estimation is waveform amplitude distribution analysis (WADA), which is based on the assumption that the speech and noise signals have Gamma and Gaussian amplitude distributions respectively. Based on previous approaches, this work uses a precomputed look-up table as a reference for SNR estimation. In this study, WADA-white (WADA-W) has been developed, which rebuilds the precomputed look-up table using a white noise profile in combination of our own AVSR database. This new data corpus, namely the Loughborough University Audio-Visual (LUNA-V) dataset that contains recordings of 10 speakers with five sets of samples uttered by each speaker is used for this experimental work. We evaluate the performance of WADA-W on this database when it is corrupted by noise generated from three profiles obtained from the NOISEX-92 database included at varying SNR values. Evaluation of performance using the LUNA-V database shows that WADA-W performs better than the original WADA in terms of SNR estimation.

WADA-W: A Modified WADA SNR Estimator for Audio-Visual Speech Recognition