TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models

• URL: http://arxiv.org/abs/2505.06660v1
• Date: Sat, 10 May 2025 14:23:37 GMT
• Title: TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models
• Authors: Junyi Peng, Takanori Ashihara, Marc Delcroix, Tsubasa Ochiai, Oldrich Plchot, Shoko Araki, Jan Černocký,
• Abstract summary: We introduce the Target-Speaker Speech Processing Universal Performance Benchmark (TS-SUPERB)<n>This benchmark includes four widely recognized target-speaker processing tasks.<n>Speaker embedding extracted from enrollment speech is used as a clue to condition downstream models.
• Score: 43.761503775097104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) models have significantly advanced speech processing tasks, and several benchmarks have been proposed to validate their effectiveness. However, previous benchmarks have primarily focused on single-speaker scenarios, with less exploration of target-speaker tasks in noisy, multi-talker conditions -- a more challenging yet practical case. In this paper, we introduce the Target-Speaker Speech Processing Universal Performance Benchmark (TS-SUPERB), which includes four widely recognized target-speaker processing tasks that require identifying the target speaker and extracting information from the speech mixture. In our benchmark, the speaker embedding extracted from enrollment speech is used as a clue to condition downstream models. The benchmark result reveals the importance of evaluating SSL models in target speaker scenarios, demonstrating that performance cannot be easily inferred from related single-speaker tasks. Moreover, by using a unified SSL-based target speech encoder, consisting of a speaker encoder and an extractor module, we also investigate joint optimization across TS tasks to leverage mutual information and demonstrate its effectiveness.

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