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arXiv · 2407.07825

RT-LA-VocE: Real-Time Low-SNR Audio-Visual Speech Enhancement

Honglie Chen·Rodrigo Mira·Stavros Petridis·Maja Pantic
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Abstract

In this paper, we aim to generate clean speech frame by frame from a live video stream and a noisy audio stream without relying on future inputs. To this end, we propose RT-LA-VocE, which completely re-designs every component of LA-VocE, a state-of-the-art non-causal audio-visual speech enhancement model, to perform causal real-time inference with a 40ms input frame. We do so by devising new visual and audio encoders that rely solely on past frames, replacing the Transformer encoder with the Emformer, and designing a new causal neural vocoder C-HiFi-GAN. On the popular AVSpeech dataset, we show that our algorithm achieves state-of-the-art results in all real-time scenarios. More importantly, each component is carefully tuned to minimize the algorithm latency to the theoretical minimum (40ms) while maintaining a low end-to-end processing latency of 28.15ms per frame, enabling real-time frame-by-frame enhancement with minimal delay.

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Honglie Chen, Rodrigo Mira, Stavros Petridis, Maja Pantic. 2024-07-10. RT-LA-VocE: Real-Time Low-SNR Audio-Visual Speech Enhancement. https://arxiv.org/abs/2407.07825

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