C4.5: programs for machine learning
C4.5: programs for machine learning
Fundamentals of speech recognition
Fundamentals of speech recognition
International Journal of Human-Computer Studies - Special issue: 1969-1999, the 30th anniversary
Template-driven generation of prosodic information for Chinese concatenative synthesis
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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Higher quality synthesized speech is required for widespread use of text-to-speech (TTS) technology, and the prosodic pattern is the key feature that makes synthetic speech sound unnatural and monotonous, which mainly describes the variation of pitch. The rules used in most Chinese TTS systems are constructed by experts, with weak quality control and low precision. In this paper, we propose a combination of clustering and machine learning techniques to extract prosodic patterns from actual large mandarin speech databases to improve the naturalness and intelligibility of synthesized speech. Typical prosody models are found by clustering analysis. Some machine learning techniques, including Rough Set, Artificial Neural Network (ANN) and Decision tree, are trained for fundamental frequency and energy contours, which can be directly used in a pitch-synchronous-overlap-add-based (PSOLA-based) TTS system. The experimental results showed that synthesized prosodic features greatly resembled their original counterparts for most syllables.