"The goal of this work is zero-shot visual voice cloning (ZS-V2C), which aims to generate speech samples with unseen speaker identity and prosody derived from a video clip and an acoustic reference. ZS-V2C presents greater challenges as: 1) unseen speaker modeling; and 2) unseen prosody modeling. Unlike previous works, we propose a novel ZS-V2C framework that incorporates a hierarchical face-styled diffusion model (HFSD-V2C). Specifically, first, we leverage cross-modal biometrics to predict unseen speaker embeddings based on facial features. Then, we jointly model the unseen prosodic features at the text, speech and video levels. Finally, a diffusion model is constructed based on the embeddings of the unseen speaker and prosodic features,enabling the generation of expressive and diverse speech. Extensive experiments on the LRS2and GRID benchmark dataset demonstrate the superior performance of our proposed method."

"This paper addresses the challenges of data scarcity and limited speaker resources in Lao-English code-switched speech synthesis. We propose a neural encoder-decoder-based method for mixed-lingual speech synthesis. The method first extracts phoneme-level speech representations and employs a dot-product attention mechanism to map Lao and English phonemes into a shared la-tent space, thereby enhancing the model’s capability to represent cross-lingual phonetic information. In addition, language ID embedding module is extended to explicitly indicate the language of each input token, helping the model distinguish and adapt to language-specific pronunciation characteristics. Experiments are conducted on the open-source English dataset LibriTTS anda proprietary Lao speech corpus. Both subjective evaluations (MOS, AB preference tests) and objective metrics (RMSE) demonstrate that the proposed approach significantly outperforms the baseline VALL-E X model in terms of naturalness and language-switching fluency. Furthermore, ablation studies confirm that both the shared phoneme latent space and the language ID mod-ule play critical roles in improving synthesis quality. This approach offers a novel solution for integrating low-resource languages into mixed-lingual speech synthesis."