SIGTYP 2021 Shared Task: Robust Spoken Language Identification

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


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.
Anthology ID:
2021.sigtyp-1.11
Volume:
Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
Month:
June
Year:
2021
Address:
Online
Venues:
NAACL | SIGTYP
SIG:
SIGTYP
Publisher:
Association for Computational Linguistics
Note:
Pages:
122–129
Language:
URL:
https://aclanthology.org/2021.sigtyp-1.11
DOI:
10.18653/v1/2021.sigtyp-1.11
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.sigtyp-1.11.pdf