Loading...
Lip-reading driven deep learning approach for speech enhancement
Adeel, Ahsan Gogate, Mandar Hussain, Amir Whitmer, William M
Adeel, Ahsan
Gogate, Mandar
Hussain, Amir
Whitmer, William M
Editors
Other contributors
Affiliation
Epub Date
Issue Date
2019-09-05
Submitted date
Files
Loading...
08825842.pdf
Adobe PDF, 3.92 MB
Alternative
Abstract
This paper proposes a novel lip-reading driven deep learning framework for speech enhancement. The proposed approach leverages the complementary strengths of both deep learning and analytical acoustic modelling (filtering based approach) as compared to recently published, comparatively simpler benchmark approaches that rely only on deep learning. The proposed audio-visual (AV) speech enhancement framework operates at two levels. In the first level, a novel deep learning-based lip-reading regression model is employed. In the second level, lip-reading approximated clean-audio features are exploited, using an enhanced, visually-derived Wiener filter (EVWF), for the clean audio power spectrum estimation. Specifically, a stacked long-short-term memory (LSTM) based lip-reading regression model is designed for clean audio features estimation using only temporal visual features considering different number of prior visual frames. For clean speech spectrum estimation, a new filterbank-domain EVWF is formulated, which exploits estimated speech features. The proposed EVWF is compared with conventional Spectral Subtraction and Log-Minimum Mean-Square Error methods using both ideal AV mapping and LSTM driven AV mapping. The potential of the proposed speech enhancement framework is evaluated under different dynamic real-world commercially-motivated scenarios (e.g. cafe, public transport, pedestrian area) at different SNR levels (ranging from low to high SNRs) using benchmark Grid and ChiME3 corpora. For objective testing, perceptual evaluation of speech quality is used to evaluate the quality of restored speech. For subjective testing, the standard mean-opinion-score method is used with inferential statistics. Comparative simulation results demonstrate significant lip-reading and speech enhancement improvement in terms of both speech quality and speech intelligibility.
Citation
Adeel, A., Gogate, M., Hussain, A. and Whitmer, W. M. (2019) Lip-reading driven deep learning approach for speech enhancement, IEEE Transactions on Emerging Topics in Computational Intelligence (forthcoming)
Research Unit
PubMed ID
PubMed Central ID
Embedded videos
Additional Links
Type
Journal article
Language
en
Description
Series/Report no.
ISSN
2471-285X
EISSN
ISBN
ISMN
Gov't Doc #
Sponsors
UK Engineering and Physical Sciences Research Council (EPSRC) Grant No. EP/M026981/1.