ISCA Archive Interspeech 2024
ISCA Archive Interspeech 2024

Binaural Selective Attention Model for Target Speaker Extraction

Hanyu Meng, Qiquan Zhang, Xiangyu Zhang, Vidhyasaharan Sethu, Eliathamby Ambikairajah

The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing. In this paper, we present a binaural time-domain Target Speaker Extraction model based on the Filter-and-Sum Network (FaSNet). Inspired by human selective hearing, our proposed model introduces target speaker embedding into separators using a multi-head attention-based selective attention block. We also compared two binaural interaction approaches – the cosine similarity of time-domain signals and inter-channel correlation in learned spectral representations. Our experimental results show that our proposed model outperforms monaural configurations and state-of-the-art multichannel target speaker extraction models, achieving best-inclass performance with 18.52 dB SI-SDR, 19.12 dB SDR, and 3.05 PESQ scores under anechoic two-speaker test configurations.