Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems

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4 Citations (Scopus)

Abstract

Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect. Current speech enhancement systems based on deep learning do not usually take into account this change in the speaking style, because they are trained with neutral (non-Lombard) speech utterances recorded under quiet conditions to which noise is artificially added. In this paper, we investigate the effects that the Lombard reflex has on the performance of audio-visual speech enhancement systems based on deep learning. The results show that a gap in the performance of as much as approximately 5 dB between the systems trained on neutral speech and the ones trained on Lombard speech exists. This indicates the benefit of taking into account the mismatch between neutral and Lombard speech in the design of audio-visual speech enhancement systems.

Original languageEnglish
Title of host publicationICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
PublisherIEEE
Publication date17 Apr 2019
Pages6615-6619
Article number8682713
ISBN (Print)978-1-4799-8132-8
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - 17 Apr 2019
Event2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
https://2019.ieeeicassp.org/

Conference

Conference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Keywords

  • Audio-visual speech enhancement
  • Lombard effect
  • deep learning

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