Disentangling Age and Identity with a Mutual Information Minimization Approach for Cross-Age Speaker Verification

Autor:	Zhang, Fengrun, Zhou, Wangjin, Liu, Yiming, Geng, Wang, Shan, Yahui, Zhang, Chen
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	There has been an increasing research interest in cross-age speaker verification~(CASV). However, existing speaker verification systems perform poorly in CASV due to the great individual differences in voice caused by aging. In this paper, we propose a disentangled representation learning framework for CASV based on mutual information~(MI) minimization. In our method, a backbone model is trained to disentangle the identity- and age-related embeddings from speaker information, and an MI estimator is trained to minimize the correlation between age- and identity-related embeddings via MI minimization, resulting in age-invariant speaker embeddings. Furthermore, by using the age gaps between positive and negative samples, we propose an aging-aware MI minimization loss function that allows the backbone model to focus more on the vocal changes with large age gaps. Experimental results show that the proposed method outperforms other methods on multiple Cross-Age test sets of Vox-CA. Comment: Interspeech 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.15974 Zobrazit plný text záznamu View this record from Arxiv