Multichannel Speech Enhancement with Own Voice-Based Interfering Speech Suppression for Hearing Assistive Devices

Poul Hoang*, Jan Mark De Haan, Zheng Hua Tan, Jesper Jensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

5 Citations (Scopus)
3 Downloads (Pure)

Abstract

Enhancementof a desired speech signal in the presence of competing or interfering speech remains an unsolved problem, as it can be hard to determine which of the speech signals is the one of interest. In this paper, we propose a multichannel noise reduction algorithm which uses the presence of the user's own voice signal, e.g. during conversations with the target speaker, as an asset to efficiently identify interfering speech and noise. Specifically, following the typical speech pattern in natural conversations, the presence of an own voice may indicate the absence of the target speech, hence undesired speech and noise can be identified and estimated during own voice presence. In contrast to conventional noise reduction systems, the proposed noise reduction systems use the user's own voice to identify interfering speech that otherwise could be confused with the target speech. We demonstrate the performance of the proposed noise reduction systems in a comparison against state-of-the-art noise reduction systems in terms of beamforming performance for hearing assistive devices. The results show that the proposed beamforming scheme in particular outperforms state-of-the-art methods in terms of ESTOI and PESQ in situations with a target speaker and a strong interfering speaker.

Original languageEnglish
JournalIEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume30
Pages (from-to)706-720
Number of pages15
ISSN2329-9290
DOIs
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by Innovation Fund Denmark under application 8053-00011A.

Publisher Copyright:
© 2014 IEEE.

Keywords

  • beamforming
  • maximum likelihood
  • speech behavior
  • Speech enhancement
  • turn-taking

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