Improvement and Assessment of Spectro-Temporal Modulation Analysis for Speech Intelligibility Estimation

Amin Edraki, Wai Yip Geoffrey Chan, Jesper Jensen, Daniel Fogerty

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

5 Downloads (Pure)

Abstrakt

Several recent high-performing intelligibility estimators of acoustically degraded speech signals employ temporal modulation analysis. In this paper, we investigate the utility of using both spectro- and temporal-modulation for estimating speech intelligibility. We modified a pre-existing speech intelligibility estimation scheme (STMI) that was inspired by human auditory spectro-temporal modulation analysis. We produced several variants of the modified STMI and assessed their intelligibility prediction accuracy, in comparison with several high-performing estimators. Among the estimators tested, one of the STMI variants and eSTOI performed consistently well on both noisy and reverberated speech. These results suggest that spectro-temporal modulation analysis is useful for certain degradation conditions such as modulated noise and reverberation.

OriginalsprogEngelsk
TitelInterspeech 2019
Antal sider5
Vol/bind2019-September
ForlagISCA
Publikationsdatosep. 2019
Sider1378-1382
DOI
StatusUdgivet - sep. 2019
BegivenhedInterspeech 2019 - Graz, Østrig
Varighed: 15 sep. 201919 sep. 2019

Konference

KonferenceInterspeech 2019
LandØstrig
ByGraz
Periode15/09/201919/09/2019
NavnProceedings of the International Conference on Spoken Language Processing
ISSN1990-9772

Fingeraftryk Dyk ned i forskningsemnerne om 'Improvement and Assessment of Spectro-Temporal Modulation Analysis for Speech Intelligibility Estimation'. Sammen danner de et unikt fingeraftryk.

  • Citationsformater

    Edraki, A., Chan, W. Y. G., Jensen, J., & Fogerty, D. (2019). Improvement and Assessment of Spectro-Temporal Modulation Analysis for Speech Intelligibility Estimation. I Interspeech 2019 (Bind 2019-September, s. 1378-1382). ISCA. Proceedings of the International Conference on Spoken Language Processing https://doi.org/10.21437/Interspeech.2019-2898