標題: An RNN-based prosodic information synthesizer for Mandarin text-to-speech
作者: Chen, SH
Hwang, SH
Wang, YR
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Mandarin;pitch contour;prosodic information synthesizer;recurrent neural network;text-to-speech
公開日期: 1-May-1998
摘要: A new RNN-based prosodic information synthesizer for Mandarin Chinese text-to-speech (TTS) is proposed in this paper. Its four-layer recurrent neural network (RNN) generates prosodic information such as syllable pitch contours, syllable energy levels, syllable initial and final durations, as well as inter-syllable pause durations. The input layer and first hidden layer operate with a word-synchronized clock to represent current-word phonologic states within the prosodic structure of text to be synthesized. The second hidden layer and output layer operate on a syllable-synchronized clock and use outputs from the preceding layers, along with additional syllable-level inputs fed directly to the second hidden layer, to generate desired prosodic parameters. The RNN was trained on a large set of actual utterances accompanied by associated texts, and can automatically learn many human-prosody phonologic rules, including the well-known Sandhi Tone 3 F0-change rule. Experimental results show that all synthesized prosodic parameter sequences matched quite well with their original counterparts, and a pitch-synchronous-overlap-add-based (PSOLA-based) Mandarin TTS system was also used for testing of our approach. While subjective tests are difficult to perform and remain to be done in the future, we have carried out informal listening tests by a significant number of native Chinese speakers and the results confirmed that all synthesized speech sounded quite natural.
URI: http://dx.doi.org/10.1109/89.668817
http://hdl.handle.net/11536/32634
ISSN: 1063-6676
DOI: 10.1109/89.668817
期刊: IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING
Volume: 6
Issue: 3
起始頁: 226
結束頁: 239
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