Analysis of duration prediction accuracy in HMM-based speech synthesis

Abstract

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.