SIGTYP 2021 Shared Task: Robust Spoken Language Identification
| العنوان: | SIGTYP 2021 Shared Task: Robust Spoken Language Identification |
|---|---|
| المؤلفون: | Ritesh Kumar, Elena Klyachko, Badr M. Abdullah, Edoardo Maria Ponti, Oleg Serikov, Elizabeth Salesky, Ryan Cotterell, Ekaterina Vylomova, Sabrina J. Mielke |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Sound (cs.SD), Domain adaptation, Computer Science - Computation and Language, Language identification, Computer science, business.industry, computer.software_genre, Computer Science - Sound, Domain (software engineering), Task (project management), Spoken language identification, Resource (project management), Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Artificial intelligence, business, Computation and Language (cs.CL), computer, Natural language processing, Electrical Engineering and Systems Science - Audio and Speech Processing |
| الوصف: | 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. The first three authors contributed equally |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ebedbf01bc7fa753a5647246e063fb6 http://arxiv.org/abs/2106.03895 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....5ebedbf01bc7fa753a5647246e063fb6 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |