Evaluating Near End Listening Enhancement Algorithms in Realistic Environments

Carol Chermaz, Cassia Valentini Botinhao, Henning Schepker, Simon King

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Speech playback (e.g., TV, radio, public address) becomes harder to understand in the presence of noise and reverberation. NELE (Near End Listening Enhancement) algorithms can improve intelligibility by modifying the signal before it is played back. Substantial intelligibility improvements have been achieved in the lab for both natural and synthetic speech. However, evidence is still scarce on how these algorithms work under conditions of realistic noise and reverberation.

We present a realistic test platform, featuring two representative everyday scenarios in which speech playback may occur (in the presence of both noise and reverberation): a domestic space (living room) and a public space (cafeteria). The generated stimuli are evaluated by measuring keyword accuracy rates in a listening test with normal hearing subjects.

We use the new platform to compare three state-of-theart NELE algorithms, employing either noise-adaptive or nonadaptive strategies, and with or without compensation for reverberation.
Original languageEnglish
Title of host publicationProceedings Interspeech 2019
PublisherInternational Speech Communication Association
Number of pages5
Publication statusPublished - 19 Sept 2019
EventInterspeech 2019 - Graz, Austria
Duration: 15 Sept 201919 Sept 2019

Publication series

PublisherInternational Speech Communication Association
ISSN (Electronic)1990-9772


ConferenceInterspeech 2019
Internet address

Keywords / Materials (for Non-textual outputs)

  • NELE
  • Near End Listening Enhancement
  • realistic noise
  • reverberation
  • speech modifications


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