Journal of Guangxi Normal University(Natural Science Edition) ›› 2024, Vol. 42 ›› Issue (4): 11-21.doi: 10.16088/j.issn.1001-6600.2023111303

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Multi-level Disentangled Personalized Speech Synthesis for Out-of-Domain Speakers Adaptation Scenarios

GAO Shengxiang1,2,3*, YANG Yuanzhang1,2, WANG Linqin1,2, MO Shangbin1,2, YU Zhengtao1,2,3, DONG Ling1,2,3   

  1. 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming Yunnan 650500, China;
    2. Yunnan Key Laboratory of Artificial Intelligence (Kunming University of Science and Technology), Kunming Yunnan 650500, China;
    3. Yunnan Key Laboratory of Media Convergence (Yunnan Daily Press Group), Kunming Yunnan 650228, China
  • Received:2023-11-13 Revised:2024-01-06 Online:2024-07-25 Published:2024-09-05

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Abstract: Personalized speech synthesis aims to generate speech with specific speaker’s characteristics. Traditional approaches often exhibit noticeable timbre disparities when synthesizing speech from unseen speakers, making it challenging to disentangle speaker-specific timbre features. This paper proposes a multi-level disentangled personalized speech synthesis approach designed for out-of-domain speakers. By fusing features at different granularities, the proposed method effectively enhances the performance of synthesizing speech from unseen speakers under zero-resource conditions. This is achieved by utilizing fast Fourier convolution to extract global speaker features, thereby enhancing the model's generalization to unseen speakers and enabling sentence-level speaker decoupling. Additionally, leveraging a speech recognition model, the method decouples speaker features at the phoneme level and captures phoneme-level timbre features through an attention mechanism, achieving phoneme-level speaker disentanglement. Experimental results on the publicly available dataset AISHELL3 demonstrate that the proposed approach achieves a cosine similarity of 0.697 for speaker feature vectors of cross-speaker adaptation, indicating a 6.25% improvement compared with the baseline model. This enhancement shows the method’s capability in modeling timbre features for speech from unseen speakers in cross-speaker adaptation scenarios.

Key words: speech synthesis, zero-shot, speaker representation, out-of-domain speaker, feature disentanglement

CLC Number:  TN912.33
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