An excitation model for HMM-based speech synthesis based on residual modeling

@inproceedings{Maia2007AnEM,
  title={An excitation model for HMM-based speech synthesis based on residual modeling},
  author={Ranniery Maia and Tomoki Toda and Heiga Zen and Yoshihiko Nankaku and Keiichi Tokuda},
  booktitle={SSW},
  year={2007}
}
This paper describes a trainable excitation approach to eliminate the unnaturalness of HMM-based speech synthesizers. During the waveform generation part, mixed excitation is constructed by state-dependent filtering of pulse trains and white noise sequences. In the training part, filters and pulse trains are jointly optimized through a procedure which resembles analysis-bysynthesis speech coding algorithms, where likelihood maximization of residual signals (derived from the same database which… CONTINUE READING
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