In this section
@inproceedings{Gudnason:2009,
author = {Gudnason, J and Thomas, MRP and Naylor, PA and Ellis, DPW},
pages = {108--111},
title = {Voice source waveform analysis and synthesis using principal component analysis and Gaussian mixture modelling},
year = {2009}
}
TY - CPAPER
AB - 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-filteirng speech with the estimated vocal tract filter. This decomposition is useful in speech analysis, synthesis, recognition and coding. Existing models of the voice source signal are based on function-fitting or physically motivated assumptions and although they are well defined, estimation of their parameters is not well understood and few are capable of reproducing the large variety of voice source waveforms. 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. Copyright © 2009 ISCA.
AU - Gudnason,J
AU - Thomas,MRP
AU - Naylor,PA
AU - Ellis,DPW
EP - 111
PY - 2009///
SP - 108
TI - Voice source waveform analysis and synthesis using principal component analysis and Gaussian mixture modelling
ER -
Speech and Audio Processing Lab
CSP Group, EEE Department
Imperial College London
Exhibition Road, London, SW7 2AZ, United Kingdom