Speech enhancement for hearing-impaired listeners using deep neural networks with auditory-model based features

Tobias Goehring, Xin Yang, Jessica J M Monaghan, Stefan Bleeck

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

5 Citations (Scopus)

Abstract

Speech understanding in adverse acoustic environments is still a major problem for users of hearinginstruments. Recent studies on supervised speech segregation show good promise to alleviate this problem by separating speech-dominated from noise-dominated spectro-temporal regions with estimated time-frequency masks. The current study compared a previously proposed feature set to a novel auditorymodel based feature set using a common deep neural network based speech enhancement framework. The performance of both feature extraction methods was evaluated with objective measurements and a subjective listening test to measure speech perception scores in terms of intelligibility and quality with 17 hearing-impaired listeners. Significant improvements in speech intelligibility and quality ratings were found for both feature extraction systems. However, the auditory-model based feature set showed superior performance compared to the comparison feature set indicating that auditory-model based processing could provide further improvements for supervised speech segregation systems and their potential applications in hearing instruments.

Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
Subtitle of host publicationproceedings
Place of PublicationBudapest, Hungary
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages2300-2304
Number of pages5
ISBN (Electronic)9780992862657
DOIs
Publication statusPublished - 28 Nov 2016
Externally publishedYes
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: 28 Aug 20162 Sep 2016

Other

Other24th European Signal Processing Conference, EUSIPCO 2016
CountryHungary
CityBudapest
Period28/08/162/09/16

Keywords

  • Auditory models
  • Deep neural networks
  • Hearing aids
  • Speech enhancement

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