Before revision: 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.
After revision: In this work, a robust and efficient text-to-speech system, named Triple M, consisting of a seq2seq model with multi-guidance attention and a multi-band multi-time LPCNet.