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

  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},
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
Highly Cited
This paper has 78 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
51 Extracted Citations
14 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 51 extracted citations

79 Citations

Citations per Year
Semantic Scholar estimates that this publication has 79 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 14 references

Similar Papers

Loading similar papers…