Gumbel-BEARD: Automated Layer Selection Framework Improves Whisper Speech Recognition in Low-Resource Domains
Researchers have proposed Gumbel-BEARD, a domain adaptation framework that automates layer selection in OpenAI's Whisper speech recognition model using a hard Gumbel-Softmax selector, enabling efficient self-supervised training in data-scarce settings. The method was tested on child speech, spontaneous speech, and African American English dialect corpora, achieving state-of-the-art word error rates. The work addresses a key bottleneck in deploying large speech models to underrepresented populations and domains where labeled data is limited.
Gumbel-BEARD is a newly proposed framework designed to adapt Whisper, a widely used speech foundation model, to low-resource domains without requiring large amounts of labeled data. The system uses an end-to-end trainable hard Gumbel-Softmax mechanism to automatically select which encoder layers to adapt, replacing the need for manual tuning. It pairs this with a BEST-RQ self-supervised objective that dynamically adjusts to the acoustic characteristics of the target domain. In experiments on the MyST child speech corpus, the method trained with only 10 hours of labeled data matched a fully supervised baseline that used the complete 133-hour labeled dataset. The framework achieved new state-of-the-art word error rates of 8.21% on MyST using Whisper-medium and 11.06% on the OGI Spontaneous dataset using Whisper-small. Evaluation on CORAAL, a corpus of African American Vernacular English, showed up to 6% relative word error rate reduction, suggesting the approach generalizes across diverse low-resource conditions. The paper has been accepted for presentation at Interspeech 2026.
What's missing
The study does not report computational costs or training time comparisons relative to the fully supervised baseline, which would be relevant for assessing practical deployability. It is also unclear how the method performs when labeled fine-tuning data is reduced below 10 hours, or whether the Gumbel-Softmax selector's layer choices are interpretable or consistent across runs. Generalization to languages other than English is not evaluated.
What different sources said
- arXiv cs.CLCenter
Gumbel-BEARD: Automatic Layer Selection for Self-Supervised Adaptation of Whisper in Low-Resource Domains
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