关键词: 语音合成, 目标代价, 声学特征, 声学模型, 拼接基元

Abstract: A general method of guiding concatenate unit selection is minimized the sum of target and concatenation cost. Since candidate units involve complex linguistic and acoustic properties, the key of improving synthesized speech quality is how to choose the acoustic（or lingustic） features that accurately represent units characteristics and constructed the corresponding target cost. This paper explores target cost construction from two aspects：acoustic characteristics（Mel-generalized cepstral, log fundamental frequency, bottle-neck feature） and acoustic models. Experimental results show that the Deep Neural Network（DNN） based acoustic model which trained by a big similar cropus and fine tuned with current cropus can predict more robust bottle-neck features, it can employ those features to participate the target cost calculation then guide optimal candidate units selection, and impove the quality of synthesized speech.

Key words: speech synthesis, target cost, acoustic features, acoustic models, concatenate unit