Detail publikace

Parameter-Efficient Tuning With Adaptive Bottlenecks For Automatic Speech Recognition

VANDERREYDT, G. PRASAD, A. KHALIL, D. MADIKERI, S. DEMUYNCK, K. MOTLÍČEK, P.

Originální název

Parameter-Efficient Tuning With Adaptive Bottlenecks For Automatic Speech Recognition

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Transfer learning from large multilingual pretrained models, like XLSR, has become the new paradigm for Automatic Speech Recognition (ASR). Considering their ever-increasing size, fine-tuning all the weights has become impractical when the computing budget is limited. Adapters are lightweight trainable modules inserted between layers while the pretrained part is kept frozen. They form a parameter-efficient fine-tuning method, but they still require a large bottleneck size to match standard fine-tuning performance. In this paper, we propose ABSADAPTER, a method to further reduce the parameter budget for equal task performance. Specifically, ABSADAPTER uses an Adaptive Bottleneck Scheduler to redistribute the adapter's weights to the layers that need adaptation the most. By training only 8% of the XLSR model, ABSADAPTER achieves close to standard fine-tuning performance on a domain-shifted Air-Traffic Communication (ATC) ASR task.

Klíčová slova

ASR, XLSR, Adapters, ATC

Autoři

VANDERREYDT, G.; PRASAD, A.; KHALIL, D.; MADIKERI, S.; DEMUYNCK, K.; MOTLÍČEK, P.

Vydáno

16. 12. 2023

Nakladatel

IEEE Signal Processing Society

Místo

Taipei

ISBN

979-8-3503-0689-7

Kniha

Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

Strany od

1

Strany do

7

Strany počet

7

URL

https://ieeexplore.ieee.org/document/10389769

BibTex

@inproceedings{BUT187932,
  author="VANDERREYDT, G. and PRASAD, A. and KHALIL, D. and MADIKERI, S. and DEMUYNCK, K. and MOTLÍČEK, P.",
  title="Parameter-Efficient Tuning With Adaptive Bottlenecks For Automatic Speech Recognition",
  booktitle="Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)",
  year="2023",
  pages="1--7",
  publisher="IEEE Signal Processing Society",
  address="Taipei",
  doi="10.1109/ASRU57964.2023.10389769",
  isbn="979-8-3503-0689-7",
  url="https://ieeexplore.ieee.org/document/10389769"
}