A Tibetan Speaker Verification Method Based on the Improved MFA-NConformer Model

© 2026 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

MFA-conformer methods are widely used in English and Chinese speaker recognition. Theoretically language-independent but practically language-related, Tibetan speaker recognition currently relies on traditional models with poor performance. To address this, we adopt MFA-conformer as the basic framework and propose improvements: integrating 1D depth-wise separable convolution and channel attention into the conformer feed-forward network, fusing multi-block features, and adding an intra-class correlation regularizer to GE2E loss. Experiments show the improved model reduces the equal error rate (EER) compared with the conformer baseline.

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