ISCA Archive Interspeech 2009
ISCA Archive Interspeech 2009

Autoregressive HMMs for speech synthesis

Matt Shannon, William Byrne

We propose the autoregressive HMM for speech synthesis. We show that the autoregressive HMM supports efficient EM parameter estimation and that we can use established effective synthesis techniques such as synthesis considering global variance with minimal modification. The autoregressive HMM uses the same model for parameter estimation and synthesis in a consistent way, in contrast to the standard HMM synthesis framework, and supports easy and efficient parameter estimation, in contrast to the trajectory HMM. We find that the autoregressive HMM gives performance comparable to the standard HMM synthesis framework on a Blizzard Challenge-style naturalness evaluation.

doi: 10.21437/Interspeech.2009-135

Cite as: Shannon, M., Byrne, W. (2009) Autoregressive HMMs for speech synthesis. Proc. Interspeech 2009, 400-403, doi: 10.21437/Interspeech.2009-135

  author={Matt Shannon and William Byrne},
  title={{Autoregressive HMMs for speech synthesis}},
  booktitle={Proc. Interspeech 2009},