Publication Details
TS-SUPERB: A Target Speech Processing Benchmark for Speech Self-Supervised Learning Models
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)
Self-supervised learning, target-speaker speech process, speech recognition, speech enhancement, voice activity detection
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.
@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 = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
year = 2025,
location = "Hyderabad, IN",
publisher = "IEEE Signal Processing Society",
ISBN = "979-8-3503-6874-1",
doi = "10.1109/ICASSP49660.2025.10887574",
language = "english",
url = "https://www.fit.vut.cz/research/publication/13522"
}