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Mixture Density Models Based on Mel-Cepstral Representation of Gaussian Process
Toru TAKAHASHI Keiichi TOKUDA Takao KOBAYASHI Tadashi KITAMURA
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2003/08/01
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Digital Signal Processing)
Gaussian mixture model, statistical framework, mel-cepstrum, EM algorithm,
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This paper defines a new kind of a mixture density model for modeling a quasi-stationary Gaussian process based on mel-cepstral representation. The conventional AR mixture density model can be applied to modeling a quasi-stationary Gaussian AR process. However, it cannot model spectral zeros. In contrast, the proposed model is based on a frequency-warped exponential (EX) model. Accordingly, it can represent spectral poles and zeros with equal weights, and, furthermore, the model spectrum has a high resolution at low frequencies. The parameter estimation algorithm for the proposed model was also derived based on an EM algorithm. Experimental results show that the proposed model has better performance than the AR mixture density model for modeling a frequency-warped EX process.