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Voice SourceWaveform Analysis and Synthesis Using Principal Component Analysis and Gaussian Mixture Modelling

Jon Gudnason; Mark R. P. Thomas; Patrick A. Naylor; Daniel P. W. Ellis

Title:
Voice SourceWaveform Analysis and Synthesis Using Principal Component Analysis and Gaussian Mixture Modelling
Author(s):
Gudnason, Jon
Thomas, Mark R. P.
Naylor, Patrick A.
Ellis, Daniel P. W.
Date:
Type:
Articles
Department:
Electrical Engineering
Permanent URL:
Book/Journal Title:
Speech and intelligence: Proceedings of Interspeech 2009
Publisher:
International Speech Communication Association
Abstract:
The paper presents a voice source waveform modeling techniques based on principal component analysis (PCA) and Gaussian mixture modeling (GMM). The voice source is obtained by inverse-filtering speech with the estimated vocal tract filter. This decomposition is useful in speech analysis, synthesis, recognition and coding. Here, a data-driven approach is presented for signal decomposition and classification based on the principal components of the voice source. The principal components are analyzed and the 'prototype' voice source signals corresponding to the Gaussian mixture means are examined. We show how an unknown signal can be decomposed into its components and/or prototypes and resynthesized. We show how the techniques are suited for both low bitrate or high quality analysis/synthesis schemes.
Subject(s):
Electrical engineering
Item views:
116
Metadata:
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