DOA or Speaker Embedding: Which is Better for Multi-Microphone Target Speaker Extraction

Abstract

Target speaker extraction (TSE) is a useful front-end to improve the speech quality and intelligibility for speech applications, whereas direction-of-arrival (DOA) and speaker embedding are two of the most often-used assistive clues to identify the target speaker in audio-only multi-microphone systems. Both can significantly improve the TSE performance compared to blind TSE models, which however have not yet been comprehensively compared in literature. In order to show their pros and cons, in this work we therefore build a unified framework for a fair comparison that allows for both DOA and speaker embedding as the assistive clue. The DOA is used to calculate multichannel spatiotemporal speech features and a speaker encoder is designed to extract the speaker embedding, either of which is then fused with the noisy speech features for TSE. We can then evaluate their respective strengths in diverse acoustic conditions, e.g., varying noise level, microphone number, speaker location. Results show that given true DOA angles, the DOA-based TSE model always outperforms the speaker embedding based counterpart regardless of noise/microphone/location conditions, meaning the stronger discriminativity of DOA in terms of speaker identity. This superiority becomes smaller if the DOA mis-match increases, and the latter can do better in the large DOA mismatch case.