Improving competing voices segregation for hearing impaired listeners using a low-latency deep neural network algorithm

Lars Bramsløw, Gaurav Naithani, Atefeh Hafez, Tom Barker, Niels Henrik Pontoppidan, Tuomas Virtanen

    Research output: Contribution to journalArticleScientificpeer-review

    7 Citations (Scopus)


    Hearing aid users are challenged in listening situations with noise and especially speech-on-speech situations with two or more competing voices. Specifically, the task of attending to and segregating two competing voices is particularly hard, unlike for normal-hearing listeners, as shown in a small sub-experiment. In the main experiment, the competing voices benefit of a deep neural network (DNN) based stream segregation enhancement algorithm was tested on hearing-impaired listeners. A mixture of two voices was separated using a DNN and presented to the two ears as individual streams and tested for word score. Compared to the unseparated mixture, there was a 13%-point benefit from the separation, while attending to both voices. If only one output was selected as in a traditional target-masker scenario, a larger benefit of 37%-points was found. The results agreed well with objective metrics and show that for hearing-impaired listeners, DNNs have a large potential for improving stream segregation and speech intelligibility in difficult scenarios with two equally important targets without any prior selection of a primary target stream. An even higher benefit can be obtained if the user can select the preferred target via remote control.

    Original languageEnglish
    Pages (from-to)172-185
    Number of pages14
    JournalJournal of the Acoustical Society of America
    Issue number1
    Publication statusPublished - 1 Jul 2018
    Publication typeA1 Journal article-refereed

    Publication forum classification

    • Publication forum level 2

    ASJC Scopus subject areas

    • Arts and Humanities (miscellaneous)
    • Acoustics and Ultrasonics


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