PronCFT: Pronunciation Contextual Fine-Tuning for Contextual Biasing of Out-of-Vocabulary Words in LLM-Based ASR

LLM-based ASR systems often replace rare and out-of-vocabulary (OOV) words with familiar words that sound similar. Two factors contribute to this error: the model has not learned how an unseen word's sound maps to its spelling, and it may favor a familiar word over the one spoken, a failure we call prior suppression. Providing candidate pronunciations alone may not help: the model can treat a phoneme annotation as a marker that raises a candidate's output probability without checking its pronunciation against the speech. We propose PronCFT, which uses fixed phoneme sequences and trains the model to transcribe speech into phonemes, match them to context candidates, and output the matched spelling. During training, we alter candidate spellings while keeping their pronunciations unchanged, discouraging the model from choosing a familiar spelling without checking how the candidate sounds. A curriculum then gradually removes intermediate outputs to reduce decoding latency and prevent phoneme transcription errors from affecting the final text. The model's retained ability to use pronunciation cues suggests that this ability can be internalized. On an OOV test set filtered from open-source benchmarks, the final model, PronCFT-C, significantly improves recall from 59.89% to 83.57% (+23.68 pp). Wrong-phoneme and spelling-interference tests indicate that the gain depends on phoneme content rather than the presence of an annotation.