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

Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson, Jesper Jensen

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4 Citationer (Scopus)

Abstrakt

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.

OriginalsprogEngelsk
TitelICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Antal sider5
ForlagIEEE
Publikationsdato17 apr. 2019
Sider6615-6619
Artikelnummer8682713
ISBN (Trykt)978-1-4799-8132-8
ISBN (Elektronisk)978-1-4799-8131-1
DOI
StatusUdgivet - 17 apr. 2019
Begivenhed2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton, Storbritannien
Varighed: 12 maj 201917 maj 2019
https://2019.ieeeicassp.org/

Konference

Konference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
LandStorbritannien
ByBrighton
Periode12/05/201917/05/2019
Internetadresse
NavnI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

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