Publication Details

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

PENG Junyi, ASHIHARA Takanori, DELCROIX Marc, OCHIAI Tsubasa, PLCHOT Oldřich, ARAKI Shoko and ČERNOCKÝ Jan. TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models. In: Proceedings of ICASSP 2025. Hyderabad: IEEE Biometric Council, 2025, pp. 1-5. ISBN 979-8-3503-6874-1. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887574
Czech title
TS-SUPERB: Sada dat a experimentů ověření zpracování řeči cílového mluvčího pomocí modelů řeči získaných samoučením
Type
conference paper
Language
english
Authors
Peng Junyi, Msc. Eng. (DCGM FIT BUT)
Ashihara Takanori (NTT)
Delcroix Marc (NTT)
Ochiai Tsubasa (NTT)
Plchot Oldřich, Ing., Ph.D. (DCGM FIT BUT)
Araki Shoko (NTT)
Černocký Jan, prof. Dr. Ing. (DCGM FIT BUT)
URL
Keywords

Self-supervised learning, target-speaker speech process, speech recognition, speech enhancement, voice activity detection

Abstract

Self-supervised learning (SSL) models have significantly
advanced speech processing tasks, and several benchmarks have been pro-
posed 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.

Published
2025
Pages
1-5
Proceedings
Proceedings of ICASSP 2025
Conference
ICASSP 2025, International Conference on Acoustics, Speech, and Signal Processing, Hyderabad, IN
ISBN
979-8-3503-6874-1
Publisher
IEEE Biometric Council
Place
Hyderabad, IN
DOI
10.1109/ICASSP49660.2025.10887574
BibTeX 
@INPROCEEDINGS{FITPUB13522,
   author = "Junyi Peng and Takanori Ashihara and Marc Delcroix and Tsubasa Ochiai and Old\v{r}ich Plchot and Shoko Araki and Jan \v{C}ernock\'{y}",
   title = "TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models",
   pages = "1--5",
   booktitle = "Proceedings of ICASSP 2025",
   year = 2025,
   location = "Hyderabad, IN",
   publisher = "IEEE Biometric Council",
   ISBN = "979-8-3503-6874-1",
   doi = "10.1109/ICASSP49660.2025.10887574",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/13522"
}
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