5 accepted papers
Audio-Visual Speech Recognition (AVSR) combines auditory and visual speech cues to enhance the accuracy and robustness of speech recognition systems. Recent advancements in AVSR have improved performance in noisy environments compared to audio-only counterparts. However, the true extent of the visua…
Turn-taking is richly multimodal. Predictive turn-taking models (PTTMs) facilitate natural- istic human-robot interaction, yet most rely solely on speech. We introduce MM-VAP, a multimodal PTTM which combines speech with visual cues including facial expression, head pose and gaze. We find that it ou…
While much of modern speech and audio processing relies on deep neural networks trained using fixed audio representations, recent studies suggest great potential in acoustic frontends learnt jointly with a backend. In this study, we focus specifically on learnable filterbanks. Prior studies have rep…
Audio-Visual Speech Recognition (AVSR) faces the difficult task of exploiting acoustic and visual cues simultaneously. Augmenting speech with the visual channel creates its own challenges, e.g. every person has unique mouth movements, making the generalization of visual models very difficult. This f…
Existing acoustic-signal-based algorithms for Voice Activity Detection (VAD) do not perform well in the presence of noise. In this study, we propose a method to improve VAD accuracy by employing another type of signal representation which is derived from the response of the human Auditory-Nerve (AN)…