Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation
Abstract:This study explores how bilingual fine-tuning affects automatic speech recognition (ASR) in low-resource languages. We evaluate this method across nine linguistically and geographically diverse language pairs, covering a range of language families and writing systems. To distinguish the two languages, during training, we pre-pend each input text with a language identification token. At inference, the model jointly predicts both the language and transcription from the speech input alone. As texts for which the language is incorrectly determined show low ASR performance, we also conduct a follow-up experiment in which the language identification token is provided both during training and inference. Our results show that bilingual fine-tuning can be beneficial when language identification accuracy is high, and that in cases where language identification performance is low, including the language identification token at inference helps to improve ASR performance.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.17820 [cs.CL] |
| (or arXiv:2606.17820v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.17820
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Generating in the Limit with Infinitely Many Hallucinations
Jun 30
-
Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
Jun 30
-
Developmental Trajectories of Situation Modeling and Mentalizing in Transformer Language Models
Jun 30
-
A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training
Jun 30
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.