2021/06/08 by Ritwik Giri, Shrikant Venkataramani, Giri, Ritwik +7 · 1 citation
Computer Science · Engineering · #Audio and Speech Processing (eess.AS) #FOS: Electrical engineering #Music and Audio Processing #Speech Recognition and Synthesis #Speech and Audio Processing #eess.AS #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.04129
INTERSPEECH 2021, 5 pages
arxiv created 2021/06/08 · openalex publication_date 2021/06/08 · arxiv updated 2021/06/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size and complexity. In this paper, we present Personalized PercepNet, a real-time speech enhancement model that separates a target speaker from a noisy multi-talker mixture without compromising on complexity of the recently proposed PercepNet. To enable speaker-dependent speech enhancement, we first show how we can train a perceptually motivated speaker embedder network to produce a representative embedding vector for the given speaker. Personalized PercepNet uses the target speaker embedding as additional information to pick out and enhance only the target speaker while suppressing all other competing sounds. Our experiments show that the proposed model significantly outperforms PercepNet and other baselines, both in terms of objective speech enhancement metrics and human opinion scores.