Precise Speech-Driven Talking-Face Synthesis with Realistic Speaker-Emulated Facial Expressions

Traditional facial animation models do not particularly stress the naturalness of imitating the dynamic facial expressions, including eye movements, dynamic eyebrow deformation, and head motion naturalness. Hence, in this paper, a speech-driven talking-face synthesizer (SDTS) is proposed for generating the dynamic talking video of a given static face for semantically mimicking the speech of any real person. The SDTS can lead the static digital-twin face to vividly mimic the expressive motions of the face and lip-synced mouth of various speakers with a personalized accent with high distinctiveness. The SDTS framework has two stages. In the first stage, one branch, termed the dynamic fused-features generation module (DFGM), contains a cross-modal speech-facial fusion module (CSFF) and a temporal convolutional network (TCN). The CSFF is the core to seamlessly align the speech features and facial features. The second branch is the self-designed adaptive identity extractor (AIE), where a series of residual blocks using partial batch normalization unit (PBN-ResNet blocks) and the residual blocks with the squeeze-and-excitation unit (SE-ResNet blocks) are cascaded to precisely capture the key features of the face in a static reference image. In the second stage of SDTS, the diffusion model termed diffusion-based rendering model (DIRM) is applied to generate the high-resolution video reconstruction with the consistencies of appearance and emotion via fusing the driving speech features and the referred facial features. The extensive experiments demonstrate that SDTS can significantly promote lip-synchronization, enrich the upper facial expression, and exhibit the naturalness of the head movements. Moreover, the SDTS can steadily maintain the facial identity consistency and the facial expression coherence for varying speaking speeds and emotions. Hence, it can attain less than 5.26 FID, 0.72 LSE-D, and 0.56 LME than the StyleTalk model, which is a well-known talking-face synthesis model.

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Publication Details

Journal
International Journal of Pattern Recognition and Artificial Intelligence
Published
2026-04-06
DOI
https://doi.org/10.1142/s0218001426570089
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Precise Speech-Driven Talking-Face Synthesis with Realistic Speaker-Emulated Facial Expressions

Din-Yuen Chan, Hsin-Chun Tsai, Shu-Yan Liou, Jhing-Fa Wang
International Journal of Pattern Recognition and Artificial Intelligence
Face recognition and analysis
article

Precise Speech-Driven Talking-Face Synthesis with Realistic Speaker-Emulated Facial Expressions

Din-Yuen Chan, Hsin-Chun Tsai, Shu-Yan Liou, Jhing-Fa Wang
article en

Abstract

Traditional facial animation models do not particularly stress the naturalness of imitating the dynamic facial expressions, including eye movements, dynamic eyebrow deformation, and head motion naturalness. Hence, in this paper, a speech-driven talking-face synthesizer (SDTS) is proposed for generating the dynamic talking video of a given static face for semantically mimicking the speech of any real person. The SDTS can lead the static digital-twin face to vividly mimic the expressive motions of the face and lip-synced mouth of various speakers with a personalized accent with high distinctiveness. The SDTS framework has two stages. In the first stage, one branch, termed the dynamic fused-features generation module (DFGM), contains a cross-modal speech-facial fusion module (CSFF) and a temporal convolutional network (TCN). The CSFF is the core to seamlessly align the speech features and facial features. The second branch is the self-designed adaptive identity extractor (AIE), where a series of residual blocks using partial batch normalization unit (PBN-ResNet blocks) and the residual blocks with the squeeze-and-excitation unit (SE-ResNet blocks) are cascaded to precisely capture the key features of the face in a static reference image. In the second stage of SDTS, the diffusion model termed diffusion-based rendering model (DIRM) is applied to generate the high-resolution video reconstruction with the consistencies of appearance and emotion via fusing the driving speech features and the referred facial features. The extensive experiments demonstrate that SDTS can significantly promote lip-synchronization, enrich the upper facial expression, and exhibit the naturalness of the head movements. Moreover, the SDTS can steadily maintain the facial identity consistency and the facial expression coherence for varying speaking speeds and emotions. Hence, it can attain less than 5.26 FID, 0.72 LSE-D, and 0.56 LME than the StyleTalk model, which is a well-known talking-face synthesis model.

International Journal of Pattern Recognition and Artificial Intelligence
National Chiayi University (TW), Sanda University (CN), National Cheng Kung University (TW), Cheng Shiu University (TW)
Openalex Percentile: Top 34%
Face recognition and analysis
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