Stream weight estimation for multistream audiovisual speech recognition in a multispeaker environment ?
The paper considers the problem of audiovisual speech recognition in a simultaneous (target/masker) speaker environment. The paper follows a conventional multistream approach and examines the specific problem of estimating reliable time-varying audio and visual stream weights. The task is challenging because, in the two speaker condition, signal-to-noise ratio (SNR)  and hence audio stream weight  cannot always be reliably inferred from the acoustics alone. Similarity between the target and masker sound sources can cause the foreground and background to be confused. The paper presents a novel solution that combines both audio and visual information to estimate acoustic SNR. The method employs artificial neural networks to estimate the SNR from hidden Markov model (HMM) state-likelihoods calculated using separate audio and visual streams. SNR estimates are then mapped to either constant utterance-level (global) stream weights or time-varying frame-based (local) stream weights. The system has been evaluated using either gender dependent models that are specific to the target speaker, or gender independent models that discriminate poorly between target and masker. When using known SNR, the time-varying stream weight system outperforms the constant stream weight systems at all SNRs tested. It is thought that the time-vary weight allows the automatic speech recognition system to take advantage of regions where local SNRs are temporally high despite the global SNR being low. When using estimated SNR the time-varying system outperformed the constant stream weight system at SNRs of 0dB and above. Systems using stream weights estimated from both audio and video information performed better than those using stream weights estimated from the audio stream alone, particularly in the gender independent case. However, when mixtures are at a global SNR below 0dB, stream weights are not sufficiently well estimated to produce good performance. Methods for improving the SNR estimation are discussed. The paper also relates the use of visual information in the current system to its role in recent simultaneous speaker intelligibility studies, where, as well as providing phonetic content, it triggers informational masking release, helping the listener to attend selectively to the target speech stream.
multistream audiovisual speech recognition multispeaker artificial neural networks likelihood