Paper
Language-Invariant Multilingual Speaker Verification for the TidyVoice 2026 Challenge
Authors
Ze Li, Xiaoxiao Miao, Juan Liu, Ming Li
Abstract
Multilingual speaker verification (SV) remains challenging due to limited cross-lingual data and language-dependent information in speaker embeddings. This paper presents a language-invariant multilingual SV system for the TidyVoice 2026 Challenge. We adopt the multilingual self-supervised w2v-BERT 2.0 model as the backbone, enhanced with Layer Adapters and Multi-scale Feature Aggregation to better exploit multi-layer representations. A language-adversarial training strategy with a Gradient Reversal Layer is applied to promote language-invariant speaker embeddings. Moreover, a multilingual zero-shot text-to-speech system is used to synthesize speech in multiple languages, improving language diversity. Experimental results demonstrate that fine-tuning the large-scale pretrained model yields competitive performance, while language-adversarial training further enhances robustness. In addition, synthetic speech augmentation provides additional gains under limited training data conditions. Source code is available at https://github.com/ZXHY-82/LI-MSV-TidyVoice2026.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.08092v1</id>\n <title>Language-Invariant Multilingual Speaker Verification for the TidyVoice 2026 Challenge</title>\n <updated>2026-03-09T08:33:13Z</updated>\n <link href='https://arxiv.org/abs/2603.08092v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.08092v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Multilingual speaker verification (SV) remains challenging due to limited cross-lingual data and language-dependent information in speaker embeddings. This paper presents a language-invariant multilingual SV system for the TidyVoice 2026 Challenge. We adopt the multilingual self-supervised w2v-BERT 2.0 model as the backbone, enhanced with Layer Adapters and Multi-scale Feature Aggregation to better exploit multi-layer representations. A language-adversarial training strategy with a Gradient Reversal Layer is applied to promote language-invariant speaker embeddings. Moreover, a multilingual zero-shot text-to-speech system is used to synthesize speech in multiple languages, improving language diversity. Experimental results demonstrate that fine-tuning the large-scale pretrained model yields competitive performance, while language-adversarial training further enhances robustness. In addition, synthetic speech augmentation provides additional gains under limited training data conditions. Source code is available at https://github.com/ZXHY-82/LI-MSV-TidyVoice2026.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='eess.AS'/>\n <published>2026-03-09T08:33:13Z</published>\n <arxiv:comment>submitted to Interspeech 2026</arxiv:comment>\n <arxiv:primary_category term='eess.AS'/>\n <author>\n <name>Ze Li</name>\n </author>\n <author>\n <name>Xiaoxiao Miao</name>\n </author>\n <author>\n <name>Juan Liu</name>\n </author>\n <author>\n <name>Ming Li</name>\n </author>\n </entry>"
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