Speech synthesis requires synthesizing the input speech text into a speech signal containing phonemes,words and utte-rances.Existing speech synthesis methods consider utterance as a whole,and it is difficult to synthesize different lengths of speech signals accurately.In this paper,we analyze the hierarchical relationships embedded in speech signals,design a Conformer-based hierarchical text encoder and a Conformer-based hierarchical speech encoder,and propose a speech synthesis model based on the hierarchical text-speech Conformer.First,the model constructs hierarchical text encoders according to the length of the input text signal,including three levels of phoneme level,word level,and utterance level text encoders.Each level of text encoder,de-scribes text information of different lengths and uses Conformer's attention mechanism to learn the relationship between different temporal features in the signal of that length.Using the hierarchical text encoder,we can find out the information that needs to be emphasized at different lengths in the utterance,and effectively achieve the extraction of text features at different lengths to alle-viate the problem of uncertainty in the duration of the synthesized speech signal.Second,the hierarchical speech encoder includes three levels:phoneme level,word level,and utterance level speech encoder.For each level of speech encoder,the text features is used as the query vector of the Conformer,and the speech features are used as the keyword vector and value vector of the Confor-mer to extract the matching relationship between text features and speech features.The problem of inaccurate synthesis of diffe-rent length speech signals can be alleviated by using hierarchical speech encoder and text-to-speech matching relations.The hie-rarchical text-to-speech encoder modeled in this paper can be flexibly embedded into a variety of existing decoders to provide more reliable speech synthesis results through the complementarity between text and speech.Experimental validation is performed on two datasets,LJSpeech and LibriTTS,and experimental results show that the Mel inversion distortion of the proposed method is smaller than that of existing speech synthesis methods.
万方数据
维普
