Abstract：A statistical acoustic model based unit selection algorithm for speech synthesis is proposed. During training stage, the acoustic models for contextual dependent phonemes are built up by using acoustic features extracted from the training data, such as spectral parameters, F0, and segmental and prosodic labels in the corpus. The hidden Markov model (HMM) is adopted as the model structure. During synthesis stage, the optimal phoneme unit sequence is searched in the speech corpus by maximizing the probabilistic likelihood between its acoustic features and the sentence HMM constructed with the contextual information of input text. Finally, the waveforms of the selected candidate units are concatenated and smoothed to produce the synthesized speech. Based on the proposed method, a Chinese speech synthesis system using initials and finals as the basic concatenation units is constructed. Results of listening test prove that the proposed method can achieve better naturalness of synthesized speech compared to the conventional method.
凌震华，王仁华. 基于统计声学模型的单元挑选语音合成算法*[J]. 模式识别与人工智能, 2008, 21(3): 280-284.
LING Zhen-Hua, WANG Ren-Hua. Statistical Acoustic Model Based Unit Selection Algorithm for Speech Synthesis. , 2008, 21(3): 280-284.
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2008, Vol. 21 
