Authors: Mengxiao Bi,Heng Lu,Shiliang Zhang,Ming Lei,Zhijie Yan
ArXiv: 1802.09194
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Abstract URL: http://arxiv.org/abs/1802.09194v1
The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among the
best parametric Text-to-Speech (TTS) systems in terms of the naturalness of
generated speech, especially the naturalness in prosody. However, the model
complexity and inference cost of BLSTM prevents its usage in many runtime
applications. Meanwhile, Deep Feed-forward Sequential Memory Networks (DFSMN)
has shown its consistent out-performance over BLSTM in both word error rate
(WER) and the runtime computation cost in speech recognition tasks. Since
speech synthesis also requires to model long-term dependencies compared to
speech recognition, in this paper, we investigate the Deep-FSMN (DFSMN) in
speech synthesis. Both objective and subjective experiments show that, compared
with BLSTM TTS method, the DFSMN system can generate synthesized speech with
comparable speech quality while drastically reduce model complexity and speech
generation time.