Although the sequence-to-sequence network with attention mechanism and neural vocoder has made great progress in the quality of speech synthesis, there are still some problems to be solved in large-scale real-time applications. For example, to avoid long sentence alignment failure while maintaining rich prosody, and to reduce the computational overhead while ensuring perceptual quality. In order to address these issues, we propose a practical neural text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet. The former uses alignment results of different attention mechanismsto guide the learning of the basic attention mechanism , and only retains the basic attention mechanism during inference. This approach can improve the performance of the text-to-feature module by absorbing the advantages of all guidance attention methods without modifying the basic inference architecture. The latter reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies . The multi-band strategy enables the LPCNet to generate sub-band signals in each inference. By predicting the sub-band signals of adjacent time in one forward operation, the multi-time strategy further decreases the number of inferences required. Due to the multi-band and multi-time strategy , the vocoder speed is increased by 2.75x on a single CPU and the MOS (mean opinion score)degradation is slight .
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) .
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Although the sequence-to-sequence network with attention mechanism and neural vocoder has made great progress in the quality of speech synthesis, there are still some problems to be solved in large-scale real-time applications. For example, to avoid long sentence alignment failure while maintaining rich prosody, and to reduce the computational overhead while ensuring perceptual quality. In order to address these issues, we propose a practical neural
In this work, a robust and efficient
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consisting of a
is proposed for large-scale online application. The key components of Triple M are: 1) A
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and a
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
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LPCNet. The former uses alignment results of different attention mechanismsto guide the learning of the basic attention mechanism , and only retains the basic attention mechanism during inference. This approach can improve the performance of the text-to-feature module by absorbing the advantages of all guidance attention methods without modifying the basic inference architecture. The latter
LPCNet, which
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. The multi-band strategy enables the LPCNet to generate sub-band signals in each inference. By predicting the sub-band signals of adjacent time in one forward operation, the multi-time strategy further decreases the number of inferences required. Due to the multi-band and multi-time strategy
(from 2.8 to 1.0 GFLOP). Due to these strategies
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and the MOS (mean opinion score)degradation is slight
without much MOS degradatiaon (4.57 vs. 4.45)