Pitch models of Mandarin text-to-speech

The function of prosody model will directly affect the naturalness of synthesized speech.Aimed at the difficulty in generating the pitch contour in prosody model,two pitch models namely corpus-based pitch model and pitch pattern model are deeply studied in this paper.Key problems in the corpus-based model are calculation of the distance and searching of the optimal path with dynamic programming algorithm.For the pitch pattern model,parameters such as pitch pattern,pitch average and pitch range are used to describe the pitch contour,and six pitch patterns are presented.For the generation of pitch contour,the pitch pattern model is more flexible than the corpus-based model.Both of the two models are linked to the real TTS system,and the MOS results of synthesized Mandarin speech show that the pitch pattern model is better than the corpus-based pitch model.

Prosodically Rich Speech Synthesis Interface Using Limited Data of Celebrity Voice

To enhance the communication between human and robots at home in the future, speech synthesis interfaces are indispensable that can generate expressive speech. In addition, synthesizing celebrity voice is commercially important. For these issues, this paper proposes techniques for synthesizing natural-sounding speech that has a rich prosodic personality using a limited amount of data in a text-to-speech (TTS) system. As a target speaker, we chose a well-known prime minister of Japan, Shinzo Abe, who has a good prosodic personality in his speeches. To synthesize natural-sounding and prosodically rich speech, accurate phrasing, robust duration prediction, and rich intonation modeling are important. For these purpose, we propose pause position prediction based on conditional random fields (CRFs), phone-duration prediction using random forests, and mora-based emphasis context labeling. We examine the effectiveness of the above techniques through objective and subjective evaluations.

Modeling Pitch Contour of Chinese Mandarin Sentences with the PENTA Model

In continuous speech, the pitch contour of the same syllable may vary much due to its contextual information. The Parallel Encoding and Target Approximation （PENTA） model is applied here to Mandarin speech synthesis with a method to predict pitch contours for Chinese syllables with different contexts by combining the Classification And Regression Tree （CART） with the PENTA model to improve its prediction accuracy. CART was first used to cluster the syllables＇ normalized pitch contours according to the syllables contextual information and the distances between pitch contours. The average pitch contour was used to train the PENTA model with the average contour for each cluster. The initial pitch is required with the PENTA model to predict a continuous pitch contour. A Pitch Discontinuity Model （PDM） was used to predict the initial pitches at positions with voiceless consonants and prosodic boundaries. Initial tests on a Chinese four-syllable word corpus containing 2048 words were extended to tests with a continuous speech corpus containing 5445 sentences. The results are satisfactory in terms of the Root Mean Square Error （RMSE） comparing the predicted pitch contour with the original contour. This method can model pitch contours for Mandarin sentences with any text for speech synthesis.