Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and
  Regional Languages Around the Globe
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.
6891c8a6c99fe2df02fc8543
Tiantian Feng
6891c8a6c99fe2df02fc8544
Kevin Huang
6891c8a6c99fe2df02fc8545
Anfeng Xu
6891c8a6c99fe2df02fc8546
Xuan Shi
6891c8a6c99fe2df02fc8547
Thanathai Lertpetchpun
6891c8a6c99fe2df02fc8548
Jihwan Lee
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Yoonjeong Lee
6891c8a6c99fe2df02fc854a
Dani Byrd
6891c8a6c99fe2df02fc854b
Shrikanth Narayanan
Voxlect: A Speech Foundation Model Benchmark for Modeling Dialects and
  Regional Languages Around the Globe
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.
Voxlect is a benchmark for evaluating speech foundation models on dialect classification and downstream applications across multiple languages and dialects.
speech foundation models dialect classification speech corpora dialectal information ASR performance speech generation systems