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Dictionary-Learning-Based Post-Filter for HMM-Based Speech Synthesis

Narayanamurthy, Praneeth Kurpad and Seelamantula, Chandra Sekhar (2015) Dictionary-Learning-Based Post-Filter for HMM-Based Speech Synthesis. In: IEEE Region 10 Conference, NOV 01-04, 2015, Macao, PEOPLES R CHINA.

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Official URL: http://dx.doi.org/10.1109/TENCON.2015.7373091

Abstract

Oversmoothing of speech parameter trajectories is one of the causes for quality degradation of HMM-based speech synthesis. Various methods have been proposed to overcome this effect, the most recent ones being global variance (GV) and modulation-spectrum-based post-filter (MSPF). However, there is still a significant quality gap between natural and synthesized speech. In this paper, we propose a two-fold post-filtering technique to alleviate to a certain extent the oversmoothing of spectral and excitation parameter trajectories of HMM-based speech synthesis. For the spectral parameters, we propose a sparse-coding-based post-filter to match the trajectories of synthetic speech to that of natural speech, and for the excitation trajectory, we introduce a perceptually motivated post-filter. Experimental evaluations show quality improvement compared with existing methods.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Depositing User: Id for Latest eprints
Date Deposited: 08 Oct 2016 07:29
Last Modified: 08 Oct 2016 07:29
URI: http://eprints.iisc.ac.in/id/eprint/54804

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