those based on mutual information maximization [667] and spatiotemporal fusion [653] have been
proposed for the lip-reading task, further expanding the methodologies explored in this domain.
In an early attempt to develop algorithms for audiovisual speech separation, the authors of
[130] proposed a CNN-based architecture that encodes facial images and speech spectrograms to
compute a complex mask for speech separation. Additionally, they introduced the AVspeech dataset
in this work. AV-CVAE [393] utilizes a conditional VAE to detect the lip movements of the speaker
and predict separated speech. In a deviation from speech signals, [385] focuses on audiovisual
singing separation and employs a two-stream CNN architecture, Y-Net [374], to process audio and
video separately. This work introduces a large dataset of solo singing videos for audiovisual singing
separation. The VisualSpeech [151] architecture takes a face image sequence and mixed audio of
AReviewofDeepLearningTechniquesforSpeechProcessing