Year: 2025
Type: conference-paper
Abstract: Modern smart glasses leverage machine learning to offer real-time transcriptions, considerably enriching human communication experiences. However, such systems frequently encounter challenges related to environmental noises, leading to decreased speech recognition. To improve voice quality, this work investigates directional source separation using the multi-microphone array. We explore multiple beamformers to assist source separation by strengthening the directional properties of speech signals. In addition to relying on predetermined beamformers, we investigate neural beamforming in multi-channel source separation, demonstrating that automatic learning directional characteristics effectively improves separation quality. Furthermore, we investigate the training strategies for ASR when utilizing separated outputs. Our results suggest that jointly training a directional speech separation and ASR model achieves the best overall performance while balancing the wearer and conversation partner’s performance.
Authors: Tiantian Feng, Ju Lin, Yiteng Huang, Weipeng He, Kaustubh Kalgaonkar, Niko Moritz, Li Wan, Xin Lei, Ming Sun, Frank Seide
Institutions: University of Southern California, Meta (United States)
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4408355612