Automatic Detection of Domain Shifts in Speech Enhancement Systems Using Confidence-Based Metrics

Lior Frankel, Shlomo E. Chazan, Jacob Goldberger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Introducing a domain shift, such as a change in language or environment, to a well-trained speech enhancement system can cause severe performance degradation. Most current research assumes that a domain shift has already been detected and focuses on either supervised or unsupervised domain adaptation techniques. Here, we address the problem of automatically detecting when a domain shift has occurred. We present a domain shift detection method based on monitoring the confidence of a network that predicts the quality of enhanced speech. The experimental results show that our method can effectively detect a domain mismatch between the training and test sets.

Original languageEnglish
Title of host publication2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings
EditorsBhaskar D Rao, Isabel Trancoso, Gaurav Sharma, Neelesh B. Mehta
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350368741
DOIs
StatePublished - 2025
Event2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Hyderabad, India
Duration: 6 Apr 202511 Apr 2025

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Conference

Conference2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Country/TerritoryIndia
CityHyderabad
Period6/04/2511/04/25

Keywords

  • domain shift
  • mismatch detection
  • speech enhancement

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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