In this work, a robust and efficient text-to-speech system, named Triple M , is proposed for large-scale online application. The key components of Triple M are: 1) A seq2seq model with multi-guidance attention which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms . Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any in-domain performance loss or online service modification. Compared with the our best result obtained by using single attention mechanism (GMM-based attention ), the word error rate of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied . 2) A  efficient multi-band multi-time LPCNet , which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies ( from 2.8 to 1.0 GFLOP ). Due to these strategies, the vocoder speed is increased by 2.75x on a single CPU without much MOS degradatiaon (4.57 vs. 4.45) .
In this work, a robust and efficient text-to-speech (TTS) synthesis system named Triple M is proposed for large-scale online application. The key components of Triple M are: 1) A sequence-to-sequence model adopts a novel multi-guidance attention to transfer complementary advantages from guiding attention mechanisms to the basic attention mechanism without in-domain performance loss and online service modification. Compared with single attention mechanism , multi-guidance attention not only brings better naturalness to long sentence synthesis, but also reduces the word error rate by 26.8\% . 2) A new efficient multi-band multi-time vocoder framework , which reduces the computational complexity from 2.8 to 1.0 GFLOP and speeds up LPCNet by 2.75x on a single CPU .
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which obtains stable feature generation and robust long sentence synthesis ability by learning from the guidance attention mechanisms . Multi-guidance attention improves the robustness and naturalness of long sentence synthesis without any
to transfer complementary advantages from guiding attention mechanisms to the basic attention mechanism without
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the our best result obtained by using
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(GMM-based attention ),
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of long sentence synthesis decreases by 23.5\%when multi-guidance attention mechanism is applied
by 26.8\%
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of LPCNet through combining multi-band and multi-time strategies (
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without much MOS degradatiaon (4.57 vs. 4.45)