Multilingual Models for ASR in Chibchan Languages

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A9JRLFHVX" target="_blank" >RIV/00216208:11320/25:9JRLFHVX - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200248556&partnerID=40&md5=4ce05a16cb985e771879e88dc9759f2c" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85200248556&partnerID=40&md5=4ce05a16cb985e771879e88dc9759f2c</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Multilingual Models for ASR in Chibchan Languages

Original language description

We present experiments on Automatic Speech Recognition (ASR) for Bribri and Cabécar, two languages from the Chibchan family. We finetune four ASR algorithms (Wav2Vec2, Whisper, MMS & WavLM) to create monolingual models, with the Wav2Vec2 model demonstrating the best performance. We then proceed to use Wav2Vec2 for (1) experiments on training joint and transfer learning models for both languages, and (2) an analysis of the errors, with a focus on the transcription of tone. Results show effective transfer learning for both Bribri and Cabécar, but especially for Bribri. A post-processing spell checking step further reduced character and word error rates. As for the errors, tone is where the Bribri models make the most errors, whereas the simpler tonal system of Cabécar is better transcribed by the model. Our work contributes to developing better ASR technology, an important tool that could facilitate transcription, one of the major bottlenecks in language documentation efforts. Our work also assesses how existing pre-trained models and algorithms perform for genuine extremely low resource-languages. ©2024 Association for Computational Linguistics.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proc. Conf. North American Chapter Assoc. Comput. Linguist.: Hum. Lang. Technol., NAACL

ISBN

979-889176114-8

ISSN

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e-ISSN

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Number of pages

15

Pages from-to

8513-8527

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Mexico City, Mexico

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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