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Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning
- Publication Year :
- 2023
-
Abstract
- In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt. This is accomplished by fine-tuning a pre-trained, end-to-end model (Whisper) to learn from demonstrations with prompt examples. We show that this ability can be generalized to different domains and even various prompt contexts, with our model gaining a Word Error Rate (WER) reduction of up to 33% on unseen datasets from various domains, such as medical conversation, air traffic control communication, and financial meetings. Considering the limited availability of audio-transcript pair data, we further extend our method to text-only fine-tuning to achieve domain sensitivity as well as domain adaptation. We demonstrate that our text-only fine-tuned model can also attend to various prompt contexts, with the model reaching the most WER reduction of 29% on the medical conversation dataset.<br />Comment: F-T Liao and Y-C Chan contributed equally; paper accepted to ASRU2023; code and model weights available in https://github.com/mtkresearch/clairaudience
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2307.10274
- Document Type :
- Working Paper