Imagine an AI that not only understands speech but can also translate languages, answer questions, summarize conversations, and even generate keywords — all without relying on massive datasets of labeled speech data. This is the vision of self-powered Large Speech-Text Models (LSMs), a new approach to training AI that leverages the model's own abilities to expand its understanding of spoken language. Traditional LSM training often stumbles on a problem called "speech anchor bias," where the AI becomes overly reliant on the audio itself, mistakenly interpreting the whole speech clip as a command. This makes it difficult for the model to follow textual instructions and limits its ability to generalize to new tasks. Researchers have discovered a clever workaround: self-powered augmentation. Instead of being trained on expensive labeled datasets, the LSM generates its own pseudo-labeled data. It uses its existing language model to process text from unlabeled ASR (Automatic Speech Recognition) datasets, augmenting it with a variety of textual instructions. This self-generated data is then used to train the LSM, essentially teaching it to better understand the relationship between spoken words and text commands. The results are impressive. Self-powered LSMs perform remarkably well across a range of speech-based tasks, outperforming models trained on large labeled datasets in some areas. This approach also improves the model's ability to align speech and text, demonstrating its potential to fuse different modalities of information. While the research highlights challenges like a performance gap compared to cascade models and the need for further dataset refinement, the innovation of self-powered learning unlocks exciting new pathways for speech AI. This new method paves the way for more robust and adaptable AI assistants, translators, and conversational agents. The potential applications are vast, offering a glimpse into a future where language is no longer a barrier for humans or machines.