关键词: 小样本生成, 语音合成, 元学习, 说话人适配, 特征提取

Abstract:

Few-shot Text-To-Speech （TTS） aims to synthesize speech that closely resembles the original speaker using only a small amount of training data. However， this approach faces challenges in quickly adapting to new speakers and improving the similarity between generated speech and speakers while ensuring high speech quality. Existing models often overlook changes in model features during different adaptation stages， leading to slow improvement of speech similarity. To address these issues， a meta-learning-guided model for adapting to new speakers was proposed. The model was guided by a meta-feature module during the adaptation process， ensuring the improvement of speech similarity while maintaining the quality of generated speech during the adaptation to new speakers. Furthermore， the differentiation of adaptation stages was achieved through a step encoder， thereby enhancing the speed of model adaptation to new speakers. The proposed method was evaluated on the Libri-TTS and VCTK datasets using subjective and objective evaluation metrics. Experimental results show that the Dynamic Time Warping-Mel Cepstral Distortion （DTW-MCD） of the proposed model are 7.450 2 and 6.524 3， respectively. It surpasses other meta-learning methods in terms of synthesized speech similarity and enables faster adaptation to new speakers.

Key words: few-shot generation, Text-To-Speech (TTS), meta-learning, speaker adaption, feature extraction