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

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.