Abstract
ABSTRACT The speech synthesis system of a particular character is a TTS (text-to-speech) synthetic system, which can obtain voice with the specific speaker’s voice characteristics. The traditional method, based on machine learning, requires a great amount of training samples and large iterations. In this paper, we proposed a novel TTS system based on fully convolutional neural networks and attention mechanism. The system can be trained start from scratch with random initialization and realize end-to-end output. By adding the attention layer and the loss of attention, it can better adapt to the features of the pronunciation, intonation and accent of a specific speaker. Experimental results show that our speech synthesis framework demonstrates a stronger model performance by synthesizing higher quality forged specific character audio with a smaller training set and lesser iterations.
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