Speech Prosody 2010
Chicago, IL, USA
Appropriate phoneme durations are essential for high quality speech synthesis. In hidden Markov model-based text-tospeech (HMM-TTS), durations are typically modeled statistically using state duration probability distributions and duration prediction for unseen contexts. Use of rich context features enables synthesis without high-level linguistic knowledge. In this paper we analyze the accuracy of state duration modeling against phone duration modeling using simple prediction techniques. In addition to the decision tree-based techniques, regression techniques for rich context features with high collinearity are discussed and evaluated.
Bibliographic reference. Silén, Hanna / Helander, Elina / Nurminen, Jani / Gabbouj, Moncef (2010): "Analysis of duration prediction accuracy in HMM-based speech synthesis", In SP-2010, paper 510.