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arXiv · 2411.13209

Comparative Analysis of Audio Feature Extraction for Real-Time Talking Portrait Synthesis

Pegah Salehi·Sajad Amouei Sheshkal·Vajira Thambawita·Sushant Gautam·Saeed S. Sabet·Dag Johansen·Michael A. Riegler·Pål Halvorsen
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Abstract

This paper examines the integration of real-time talking-head generation for interviewer training, focusing on overcoming challenges in Audio Feature Extraction (AFE), which often introduces latency and limits responsiveness in real-time applications. To address these issues, we propose and implement a fully integrated system that replaces conventional AFE models with Open AI's Whisper, leveraging its encoder to optimize processing and improve overall system efficiency. Our evaluation of two open-source real-time models across three different datasets shows that Whisper not only accelerates processing but also improves specific aspects of rendering quality, resulting in more realistic and responsive talking-head interactions. These advancements make the system a more effective tool for immersive, interactive training applications, expanding the potential of AI-driven avatars in interviewer training.

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Pegah Salehi, Sajad Amouei Sheshkal, Vajira Thambawita, Sushant Gautam, Saeed S. Sabet, Dag Johansen, Michael A. Riegler, Pål Halvorsen. 2024-11-20. Comparative Analysis of Audio Feature Extraction for Real-Time Talking Portrait Synthesis. https://arxiv.org/abs/2411.13209

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