Improving low-resource ASR using bilingual fine-tuning with language identification: a cross-linguistic evaluation
arXiv:2606.17820v1 Announce Type: new 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 sh
This research addresses the ongoing challenge of developing robust AI models for languages with limited data, a critical bottleneck for global AI accessibility and equity.
Improving ASR in low-resource languages can significantly broaden AI's utility and economic impact beyond major linguistic blocs, fostering more inclusive technological advancement.
The ability to more effectively train ASR models for a wider array of languages could accelerate the deployment of voice-enabled AI and services globally.
- · AI developers in non-English speaking markets
- · Organizations targeting emerging markets
- · Linguistic diversity advocates
- · Speech technology companies
- · Monolingual AI services
- · Those reliant solely on high-resource language data
Wider adoption of AI-powered services in previously underserved linguistic communities.
Increased demand for curated datasets and language experts for low-resource languages, spurring new data economies.
Enhanced digital inclusion and economic participation for speakers of historically marginalized languages, potentially reducing digital divides.
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