Speaker-dependent Dictionary-based Speech Enhancement for Text-Dependent Speaker Verification

Nicolai Bæk Thomsen, Dennis Alexander Lehmann Thomsen, Zheng-Hua Tan, Børge Lindberg, Søren Holdt Jensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

4 Citations (Scopus)


The problem of text-dependent speaker verification under noisy conditions is becoming ever more relevant, due to increased
usage for authentication in real-world applications.
Classical methods for noise reduction such as spectral subtraction and Wiener filtering introduce distortion and do not perform well in
this setting.
In this work we compare the performance of different noise reduction methods under different noise conditions
in terms of speaker verification when the text is known and the system is trained on clean data (mis-matched conditions).
We furthermore propose a new approach based on dictionary-based noise reduction and compare it to the baseline methods.
Original languageEnglish
Title of host publicationInterspeech 2016
Publication dateSep 2016
Publication statusPublished - Sep 2016
EventInterspeech 2016 - San Francisco, CA, United States
Duration: 8 Sep 201612 Sep 2016


ConferenceInterspeech 2016
CountryUnited States
CitySan Francisco, CA
Internet address


  • Noise reduction
  • speaker verification
  • dictionary learning

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