Analysis of Duration Prediction Accuracy in HMM-Based Speech Synthesis


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Silén, Hanna; Helander, Elina; Nurminen, Jani; Gabbouj, Moncef

Abstract

Appropriate phoneme durations are essential for high quality speech synthesis. In hidden Markov model-based text-to-speech (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.

Keywords

speech synthesis

Research areas

Year:
2010
Book title:
The Fifth International Conference on Speech Prosody
Month:
May