Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912518074531840 |
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| author | Feng, Tiantian Huang, Kevin Xu, Anfeng Shi, Xuan Lertpetchpun, Thanathai Lee, Jihwan Lee, Yoonjeong Byrd, Dani Narayanan, Shrikanth |
| author_facet | Feng, Tiantian Huang, Kevin Xu, Anfeng Shi, Xuan Lertpetchpun, Thanathai Lee, Jihwan Lee, Yoonjeong Byrd, Dani Narayanan, Shrikanth |
| contents | We present Voxlect, a novel benchmark for modeling dialects and regional languages worldwide using speech foundation models. Specifically, we report comprehensive benchmark evaluations on dialects and regional language varieties in English, Arabic, Mandarin and Cantonese, Tibetan, Indic languages, Thai, Spanish, French, German, Brazilian Portuguese, and Italian. Our study used over 2 million training utterances from 30 publicly available speech corpora that are provided with dialectal information. We evaluate the performance of several widely used speech foundation models in classifying speech dialects. We assess the robustness of the dialectal models under noisy conditions and present an error analysis that highlights modeling results aligned with geographic continuity. In addition to benchmarking dialect classification, we demonstrate several downstream applications enabled by Voxlect. Specifically, we show that Voxlect can be applied to augment existing speech recognition datasets with dialect information, enabling a more detailed analysis of ASR performance across dialectal variations. Voxlect is also used as a tool to evaluate the performance of speech generation systems. Voxlect is publicly available with the license of the RAIL family at: https://github.com/tiantiaf0627/voxlect. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_01691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe Feng, Tiantian Huang, Kevin Xu, Anfeng Shi, Xuan Lertpetchpun, Thanathai Lee, Jihwan Lee, Yoonjeong Byrd, Dani Narayanan, Shrikanth Sound Computation and Language Audio and Speech Processing We present Voxlect, a novel benchmark for modeling dialects and regional languages worldwide using speech foundation models. Specifically, we report comprehensive benchmark evaluations on dialects and regional language varieties in English, Arabic, Mandarin and Cantonese, Tibetan, Indic languages, Thai, Spanish, French, German, Brazilian Portuguese, and Italian. Our study used over 2 million training utterances from 30 publicly available speech corpora that are provided with dialectal information. We evaluate the performance of several widely used speech foundation models in classifying speech dialects. We assess the robustness of the dialectal models under noisy conditions and present an error analysis that highlights modeling results aligned with geographic continuity. In addition to benchmarking dialect classification, we demonstrate several downstream applications enabled by Voxlect. Specifically, we show that Voxlect can be applied to augment existing speech recognition datasets with dialect information, enabling a more detailed analysis of ASR performance across dialectal variations. Voxlect is also used as a tool to evaluate the performance of speech generation systems. Voxlect is publicly available with the license of the RAIL family at: https://github.com/tiantiaf0627/voxlect. |
| title | Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and Regional Languages Around the Globe |
| topic | Sound Computation and Language Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.01691 |