Robust Target Speaker Diarization and Separation via Augmented Speaker Embedding Sampling

• URL: http://arxiv.org/abs/2508.06393v1
• Date: Fri, 08 Aug 2025 15:24:10 GMT
• Title: Robust Target Speaker Diarization and Separation via Augmented Speaker Embedding Sampling
• Authors: Md Asif Jalal, Luca Remaggi, Vasileios Moschopoulos, Thanasis Kotsiopoulos, Vandana Rajan, Karthikeyan Saravanan, Anastasis Drosou, Junho Heo, Hyuk Oh, Seokyeong Jeong,
• Abstract summary: This work introduces a new approach to train simultaneous speech separation and diarization using automatic identification of target speaker embeddings.<n>Our proposed model employs a dual-stage training pipeline designed to learn robust speaker representation features.<n>We present an overlapping spectral loss function specifically tailored for enhancing diarization accuracy during overlapped speech frames.
• Score: 4.875137823752148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional speech separation and speaker diarization approaches rely on prior knowledge of target speakers or a predetermined number of participants in audio signals. To address these limitations, recent advances focus on developing enrollment-free methods capable of identifying targets without explicit speaker labeling. This work introduces a new approach to train simultaneous speech separation and diarization using automatic identification of target speaker embeddings, within mixtures. Our proposed model employs a dual-stage training pipeline designed to learn robust speaker representation features that are resilient to background noise interference. Furthermore, we present an overlapping spectral loss function specifically tailored for enhancing diarization accuracy during overlapped speech frames. Experimental results show significant performance gains compared to the current SOTA baseline, achieving 71% relative improvement in DER and 69% in cpWER.

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