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Lafferty, J., McCallum, A. and Pereira, F.C. (2001) Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. 18th International Conference on Machine Learning, Williamstown, 28 June-1 July 2001, 282-289.

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• TITLE: Prosodically Rich Speech Synthesis Interface Using Limited Data of Celebrity Voice

  AUTHORS: Takashi Nose, Taiki Kamei

  KEYWORDS: Parametric Speech Synthesis, Hidden Markov Model (HMM), Prosodic Personality, Prosody Modeling, Conditional Random Field (CRF), Random Forest, Emphasis Context

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.16, December 29, 2016

  ABSTRACT: 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.