Before revision:  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 .
After revision:  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, which reduces the computational complexity of LPCNet through combining multi-band and multi-time strategies .