The development of Bimodal Recurrent Neural Networks (BRNN) has led to significant ad-
vancements in the field of Audiovisual Speech Activity Detection (AV-SAD) [531]. BRNNs have
demonstrated immense potential in improving the performance of speech recognition systems,
particularly in noisy environments, by combining information from various sources. By integrating
separate RNNs for each modality, BRNNs can capture temporal dependencies within and across
modalities. This leads to successful outcomes in speech-based systems, where integrating audio and
visual modalities is crucial for accurate speech recognition. Compared to conventional audio-only
systems, BRNN-based AV-SAD systems display superior performance, particularly in challenging
acoustic conditions where audio-only systems might struggle.
AReviewofDeepLearningTechniquesforSpeechProcessing