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arXiv · 2407.17902

Multi-Stage Face-Voice Association Learning with Keynote Speaker Diarization

Ruijie Tao·Zhan Shi·Yidi Jiang·Duc-Tuan Truong·Eng-Siong Chng·Massimo Alioto·Haizhou Li
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Abstract

The human brain has the capability to associate the unknown person's voice and face by leveraging their general relationship, referred to as ``cross-modal speaker verification''. This task poses significant challenges due to the complex relationship between the modalities. In this paper, we propose a ``Multi-stage Face-voice Association Learning with Keynote Speaker Diarization''~(MFV-KSD) framework. MFV-KSD contains a keynote speaker diarization front-end to effectively address the noisy speech inputs issue. To balance and enhance the intra-modal feature learning and inter-modal correlation understanding, MFV-KSD utilizes a novel three-stage training strategy. Our experimental results demonstrated robust performance, achieving the first rank in the 2024 Face-voice Association in Multilingual Environments (FAME) challenge with an overall Equal Error Rate (EER) of 19.9%. Details can be found in https://github.com/TaoRuijie/MFV-KSD.

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Ruijie Tao, Zhan Shi, Yidi Jiang, Duc-Tuan Truong, Eng-Siong Chng, Massimo Alioto, Haizhou Li. 2024-07-25. Multi-Stage Face-Voice Association Learning with Keynote Speaker Diarization. https://arxiv.org/abs/2407.17902

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