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Articulatory based speech models for blind speech dereverberation using sequential Monte Carlo methods

Articulatory based speech models for blind speech dereverberation using sequential Monte Carlo methods
Articulatory based speech models for blind speech dereverberation using sequential Monte Carlo methods

Room reverberation leads to reduced intelligibility of audio signals. Enhancement is thus crucial for high-quality audio and scene analysis applications. This paper proposes to directly and optimally estimate the source signal and acoustic channel from the distorted observations. The remaining model parameters are sampled from a particle filter, facilitating real-time dereverberation. The approach was previously successfully applied to single- and multisensor blind dereverberation. Enhancement can be improved upon by accurately modelling the speech production system. This paper therefore extends the blind dereverberation approach to incorporate a novel source model based on parallel formant synthesis and compares the approach to one using a time-varying AR model, with parameters varying according to a random walk. Experimental data shows that dereverberation using the proposed model is improved for vowels, stop consonants, and fricatives.

2131-2135
European Signal Processing Conference
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Hopgood, James R.
ae180a4d-33bf-468d-ab66-5eeb152e7fc2
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Hopgood, James R.
ae180a4d-33bf-468d-ab66-5eeb152e7fc2

Evers, Christine and Hopgood, James R. (2010) Articulatory based speech models for blind speech dereverberation using sequential Monte Carlo methods. In European Signal Processing Conference. European Signal Processing Conference. pp. 2131-2135 .

Record type: Conference or Workshop Item (Paper)

Abstract

Room reverberation leads to reduced intelligibility of audio signals. Enhancement is thus crucial for high-quality audio and scene analysis applications. This paper proposes to directly and optimally estimate the source signal and acoustic channel from the distorted observations. The remaining model parameters are sampled from a particle filter, facilitating real-time dereverberation. The approach was previously successfully applied to single- and multisensor blind dereverberation. Enhancement can be improved upon by accurately modelling the speech production system. This paper therefore extends the blind dereverberation approach to incorporate a novel source model based on parallel formant synthesis and compares the approach to one using a time-varying AR model, with parameters varying according to a random walk. Experimental data shows that dereverberation using the proposed model is improved for vowels, stop consonants, and fricatives.

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More information

Published date: 1 December 2010
Venue - Dates: 18th European Signal Processing Conference, EUSIPCO 2010, , Aalborg, Denmark, 2010-08-22 - 2010-08-26

Identifiers

Local EPrints ID: 445886
URI: http://eprints.soton.ac.uk/id/eprint/445886
PURE UUID: 995cd4bc-c3ce-46d8-ae8a-27a5c5a89642
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 13 Jan 2021 17:31
Last modified: 18 Feb 2021 17:41

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Contributors

Author: Christine Evers ORCID iD
Author: James R. Hopgood

University divisions

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