Abstract

Abstract — Visual information from a speaker’s mouth region is known to improve automatic speech recognition robustness. However, the vast majority of audio-visual automatic speech recognition (AVASR) studies assume frontal images of the speaker’s face. In contrast, this paper investigates extracting visual speech information from the speaker’s profile view, and, to our knowledge, constitutes the first real attempt to attack this problem. As with any AVASR system, the overall recognition performance depends heavily on the visual front end. This is especially the case with profile-view data, as the facial features are heavily compacted compared to the frontal scenario. In this paper, we particularly describe our visual front end approach, and report experiments on a multi-subject, small-vocabulary, bimodal, multisensory database that contains synchronously captured audio with frontal and profile face video. Our experiments demonstrate that AVASR is possible from profile views, however the visual modality benefit is decreased compared to frontal video data. I.

Citations