关键词: 语音数据筛选, 声学模型训练, 基于HMM的单元挑选与波形拼接

Abstract: Aiming at roughly labeled corpora with several hundred hours of speech, a novel approach of constructing text-to-speech system is proposed. This approach realizes automatically cleaning and labeling of large-scale corpora by means of speech recognition, text alignment and syntactic parsing. Furthermore, in order to solve the problems of memory space expansion and time consumption for acoustic model training of large-scale corpora, a fast training method, which can ensure the accuracy of acoustic model, is realized through the optimization of conventional process of model training. Subjective evaluations show that the exploitation of large-scale corpora with rough transcription can achieve significant improvement at 0.5 MOS score in contrast with small-scale corpora with exact transcription.

Key words: speech data selection, acoustic model training, HMM-based unit selection and waveform concatenation