Learning New Words from Unlabeled Test Data in Automatic Speech Recognition
New words are invented every day. A human listener can learn a new word by hearing it clearly once and inferring its usage from sentence context. This paper proposes granting ASR a similar ability to learn the contextual representations and spellings of new words from unlabeled test data at test time. A frozen CTC acoustic model provides spellings, a frozen language model provides contextual evidence for out-of-vocabulary (OOV) word detection, and an adaptation module expands the vocabulary by learning the lexical token representations with distributions over CTC-generated candidates. The spelling model of each token is optimized by minimizing a Kullback-Leibler divergence (KLD) objective...
| Authors | Mengqi Wang, Mark A. Hasegawa-Johnson, Haolong Zheng et al. |
|---|---|
| Primary category | eess.AS |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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