Prompts are crucial to large language models as they provide context information such as topic or logical relationships. Inspired by this, we propose PromptASR, a framework that integrates prompts in end-to-end automatic speech recognition (E2E ASR) systems to achieve contextualized ASR with controllable style of transcriptions. Specifically, a dedicated text encoder encodes the text prompts and the encodings are injected into the speech encoder by cross-attending the features from two modalities. When using the ground truth text from preceding utterances as content prompt, the proposed system achieves 21.9% and 6.8% relative word error rate reductions on a book reading dataset and an in-house dataset compared to a baseline ASR system. The system can also take word-level biasing lists as prompt to improve recognition accuracy on rare words. An additional style prompt can be given to the text encoder and guide the ASR system to output different styles of transcriptions. The code is available at icefall.

How do I land the role and with what resources exactly in 6 months? I have no surprises that ChatGPT did well here. It is able to regurgigate oft-repeated advice for career changers. While this is technically correct, I didn't find it overwhelming impressive. Side note: One can combine this tip with Tip 41 (Suggest portfolio ideas) and 42 (Suggest dataset) when building one's portfolio.