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The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among thebest parametric Text-to-Speech (TTS) systems in terms of the naturalness ofgenerated speech, especially the naturalness in prosody. However, the modelcomplexity and inference cost of BLSTM prevents its usage in many runtimeapplications. 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. Sincespeech synthesis also requires to model long-term dependencies compared tospeech recognition, in this paper, we investigate the Deep-FSMN (DFSMN) inspeech synthesis. Both objective and subjective experiments show that, comparedwith BLSTM TTS method, the DFSMN system can generate synthesized speech withcomparable speech quality while drastically reduce model complexity and speechgeneration time.

Deep Feed-forward Sequential Memory Networks for Speech Synthesis