HMM-Based Speech Enhancement Using Sub-Word Models and Noise Adaptation

Kato, Akihiro and Milner, Ben (2016) HMM-Based Speech Enhancement Using Sub-Word Models and Noise Adaptation. In: Proceedings of the Interspeech Conference 2016. International Speech Communication Association, USA, pp. 3748-3752.

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Abstract

This work proposes a method of speech enhancement that uses a network of HMMs to first decode noisy speech and to then synthesise a set of features that enables a clean speech signal to be reconstructed. Different choices of acoustic model (whole-word, monophone and triphone) and grammars (highly constrained to no constraints) are considered and the effects of introducing or relaxing acoustic and grammar constraints investigated. For robust operation in noisy conditions it is necessary for the HMMs to model noisy speech and consequently noise adaptation is investigated along with its effect on the reconstructed speech. Speech quality and intelligibility analysis find triphone models with no grammar, combined with noise adaptation, gives highest performance that outperforms conventional methods of enhancement at low signal-to-noise ratios.

Item Type:	Book Section
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Pure Connector
Date Deposited:	01 Dec 2016 00:05
Last Modified:	10 Dec 2024 01:11
URI:	https://ueaeprints.uea.ac.uk/id/eprint/61574
DOI:	10.21437/Interspeech.2016-928

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