Imagine a world where voice assistants flawlessly understand you, even in noisy environments. That's the promise of robust speech recognition, and researchers are making strides with a fascinating new technique using "transcription prompts." Traditional speech AI models often mishear or repeat parts of speech, especially when there's background noise. This happens because they can struggle to make sense of what they're hearing in context and the text isn't necessarily aligned well with the audio. This new research introduces a clever trick: feeding the model a transcript alongside the raw audio. This transcript acts like a guide, helping the AI understand the audio's meaning and structure before interpreting it. Like giving someone a cheat sheet before a test. This approach significantly improves accuracy, reducing errors even in challenging situations like online meetings. The research also tackles the problem of repetitive errors—those times when the speech AI gets stuck in a loop. By incorporating the transcription prompt and combining two methods, Autoregressive (AR) and Non-autoregressive (NAR), this repetition problem is fundamentally addressed. While still under development, this research opens exciting possibilities. More accurate speech AI means better voice assistants, more reliable transcription services, and seamless communication between humans and machines. The next step? Exploring different types of prompts and refining the technology for real-world applications.