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. Existing models of the voice source signal are based on functionfitting 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 eprototypef 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.
Bibliographic reference. Gudnason, Jon / Thomas, Mark R. P. / Naylor, Patrick A. / Ellis, Dan P. W. (2009): "Voice source waveform analysis and synthesis using principal component analysis and Gaussian mixture modelling", In INTERSPEECH-2009, 108-111.