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arXiv · 2106.03895

SIGTYP 2021 Shared Task: Robust Spoken Language Identification

Elizabeth Salesky·Badr M. Abdullah·Sabrina J. Mielke·Elena Klyachko·Oleg Serikov·Edoardo Ponti·Ritesh Kumar·Ryan Cotterell·Ekaterina Vylomova
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Abstract

While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource and endangered languages this is in part due to resource availability: where larger datasets exist, they may be single-speaker or have different domains than desired application scenarios, demanding a need for domain and speaker-invariant language identification systems. This year's shared task on robust spoken language identification sought to investigate just this scenario: systems were to be trained on largely single-speaker speech from one domain, but evaluated on data in other domains recorded from speakers under different recording circumstances, mimicking realistic low-resource scenarios. We see that domain and speaker mismatch proves very challenging for current methods which can perform above 95% accuracy in-domain, which domain adaptation can address to some degree, but that these conditions merit further investigation to make spoken language identification accessible in many scenarios.

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Elizabeth Salesky, Badr M. Abdullah, Sabrina J. Mielke, Elena Klyachko, Oleg Serikov, Edoardo Ponti, Ritesh Kumar, Ryan Cotterell, Ekaterina Vylomova. 2021-06-07. SIGTYP 2021 Shared Task: Robust Spoken Language Identification. https://arxiv.org/abs/2106.03895

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