Editing State-of-the-art (section)

==== Wang S, Yang C H H, Wu J, et al. Can whisper perform speech-based in-context learning[J]. arXiv preprint arXiv:2309.07081, 2023. ====

* Summary: The study investigates Whisper ASR models' in-context learning capabilities and proposes a novel SICL method for test-time adaptation without gradient descent, achieving significant WER reductions.
* RQ: The research explores whether Whisper models can perform speech-based in-context learning and how to leverage in-context examples for test-time adaptation efficiently.
* Hypothesis: The hypothesis is that Whisper models can adapt at test time using SICL with context examples from specific dialects or speakers.
* Conclusion: SICL significantly improves ASR performance for Chinese dialects without gradient descent, with k-NN enhancing SICL's efficiency.
* Critical observations: Correct LID settings and k-NN example selection improve Whisper's inference, with language-level adaptation outperforming speaker-level adaptation.
* Relevance: The study is relevant for understanding and enhancing the application of large pre-trained models in automatic speech recognition and dialect adaptation.