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    <abstract>Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have struggled to generalize across multilingual environments, leading to significant performance degradation when applied to multiple languages. In this paper, we propose an advanced approach to multilingual speaker identification and verification, specifically designed for Indo-Aryan and Dravidian languages. Empirical results on the Kathbath dataset show that our approach significantly improves speaker identification accuracy, reducing the performance gap between monolingual and multilingual systems from 15% to just 1%. Additionally, our model reduces the equal error rate for speaker verification from 15% to 5% in noisy conditions. Our method demonstrates strong generalization capabilities across diverse languages, offering a scalable solution for multilingual voice-based biometric systems.</abstract>
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%0 Conference Proceedings
%T Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages
%A Sritharan, Braveenan
%A Thayasivam, Uthayasanker
%Y Weerasinghe, Ruvan
%Y Anuradha, Isuri
%Y Sumanathilaka, Deshan
%S Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F sritharan-thayasivam-2025-advancing
%X Multilingual speaker identification and verification is a challenging task, especially for languages with diverse acoustic and linguistic features such as Indo-Aryan and Dravidian languages. Previous models have struggled to generalize across multilingual environments, leading to significant performance degradation when applied to multiple languages. In this paper, we propose an advanced approach to multilingual speaker identification and verification, specifically designed for Indo-Aryan and Dravidian languages. Empirical results on the Kathbath dataset show that our approach significantly improves speaker identification accuracy, reducing the performance gap between monolingual and multilingual systems from 15% to just 1%. Additionally, our model reduces the equal error rate for speaker verification from 15% to 5% in noisy conditions. Our method demonstrates strong generalization capabilities across diverse languages, offering a scalable solution for multilingual voice-based biometric systems.
%U https://aclanthology.org/2025.indonlp-1.8/
%P 67-73

Markdown (Informal)

[Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages](https://aclanthology.org/2025.indonlp-1.8/) (Sritharan & Thayasivam, IndoNLP 2025)

• Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages (Sritharan & Thayasivam, IndoNLP 2025)

ACL

• Braveenan Sritharan and Uthayasanker Thayasivam. 2025. Advancing Multilingual Speaker Identification and Verification for Indo-Aryan and Dravidian Languages. In Proceedings of the First Workshop on Natural Language Processing for Indo-Aryan and Dravidian Languages, pages 67–73, Abu Dhabi. Association for Computational Linguistics.