Looking to listen at the cocktail party

TLDR

Audio‑only speech separation struggles to associate separated signals with speakers in video. The study introduces a deep audio‑visual model that isolates a target speaker’s speech from mixtures by combining visual and auditory cues. The model uses visual features to focus audio on target speakers and is trained on the AVS peech dataset of thousands of hours of web video. The model outperforms audio‑only and speaker‑dependent audio‑visual baselines, successfully isolating target speech in diverse real‑world settings by simply specifying the speaker’s face.

Abstract

We present a joint audio-visual model for isolating a single speech signal from a mixture of sounds such as other speakers and background noise. Solving this task using only audio as input is extremely challenging and does not provide an association of the separated speech signals with speakers in the video. In this paper, we present a deep network-based model that incorporates both visual and auditory signals to solve this task. The visual features are used to "focus" the audio on desired speakers in a scene and to improve the speech separation quality. To train our joint audio-visual model, we introduce AVS peech , a new dataset comprised of thousands of hours of video segments from the Web. We demonstrate the applicability of our method to classic speech separation tasks, as well as real-world scenarios involving heated interviews, noisy bars, and screaming children, only requiring the user to specify the face of the person in the video whose speech they want to isolate. Our method shows clear advantage over state-of-the-art audio-only speech separation in cases of mixed speech. In addition, our model, which is speaker-independent (trained once, applicable to any speaker), produces better results than recent audio-visual speech separation methods that are speaker-dependent (require training a separate model for each speaker of interest).