Style-Code Method for Multi-Style Parametric Text-to-Speech Synthesis

Abstract

Modern text-to-speech systems generally achieve good intelligibility. The one of the main drawbacks of these systems is the lack of expressiveness in comparison to natural human speech. It is very unpleasant when automated system conveys positive and negative message in completely the same way. The introduction of parametric methods in speech synthesis gave possibility to easily change speaker characteristics and speaking styles. In this paper a simple method for incorporating styles into synthesized speech by using style codes is presented. The proposed method requires just a couple of minutes of target style and moderate amount of neutral speech. It is successfully applied to both hidden Markov models and deep neural networks-based synthesis, giving style code as additional input to the model. Listening tests confirmed that better style expressiveness is achieved by deep neural networks synthesis compared to hidden Markov model synthesis. It is also proved that quality of speech synthesized by deep neural networks in a certain style is comparable with the speech synthesized in neutral style, although the neutral-speech-database is about 10 times bigger. DNN based TTS with style codes are further investigated by comparing the quality of speech produced by single-style modeling and multi-style modeling systems. Objective and subjective measures confirmed that there is no significant difference between these two approaches.