Speech-In-Noise Comprehension is Improved When Viewing a Deep-Neural-Network-Generated Talking Face

Abstract

Listening in a noisy environment is challenging, but many previous studies have demonstrated that comprehension of speech can be substantially improved by looking at the talker's face. We recently developed a deep neural network (DNN) based system that generates movies of a talking face from speech audio and a single face image. In this study, we aimed to quantify the benefits that such a system can bring to speech comprehension, especially in noise. The target speech audio was masked with signal to noise ratios of −9, −6, −3, and 0 dB and was presented to subjects in three audio-visual (AV) stimulus conditions: (1) synthesized AV: audio with the synthesized talking face movie; (2) natural AV: audio with the original movie from the corpus; and (3) audio-only: audio with a static image of the talker. Subjects were asked to type the sentences they heard in each trial and keyword recognition was quantified for each condition. Overall, performance in the synthesized AV condition fell approximately halfway between the other two conditions, showing a marked improvement over the audio-only control but still falling short of the natural AV condition. Every subject showed some benefit from the synthetic AV stimulus. The results of this study support the idea that a DNN-based model that generates a talking face from speech audio can meaningfully enhance comprehension in noisy environments, and has the potential to be used as a visual hearing aid.