Imagine teaching a computer to understand and follow spoken instructions, not by explicitly training it on countless examples, but by leveraging its existing knowledge of language. That's the exciting premise behind a new approach to building speech language models (SLMs), detailed in the research paper "Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data." Traditionally, training SLMs, AI systems designed to process and respond to voice commands, has been a laborious process. It involved 'instruction-tuning' with massive datasets of speech-text pairs, essentially teaching the model to understand specific instructions and their corresponding actions in the audio modality. This is not only resource-intensive but also carries the risk of the model 'forgetting' previously learned language skills. The new research challenges this convention. Instead of direct instruction-tuning, researchers created a unique process for generating speech-text pairs that focus on 'paralinguistic' elements of speech – aspects like tone, emotion, and accent that add depth and meaning beyond just the words themselves. Their model, dubbed DeSTA2 (Descriptive Speech-Text Alignment), uses an intriguing method. It leverages a pre-trained large language model (LLM) to create descriptions of speech audio based on extracted speech metadata. This metadata, sourced from existing datasets and specialized speech models, includes information like gender, emotion, accent, and even audio quality metrics. DeSTA2 then uses this descriptive data to train a combined speech-text model, effectively teaching it to understand the nuances of speech by describing what's happening in the audio. The results are impressive. DeSTA2 performs remarkably well on benchmarks like Dynamic-SUPERB and AIR-Bench-Chat, even surpassing models trained with traditional instruction-tuning. It demonstrates a capacity not just for understanding words but also for deciphering the underlying emotions and intentions within the speech. Furthermore, DeSTA2 inherits the reasoning abilities of its underlying LLM, meaning it can follow complex, multi-step instructions and even perform chain-of-thought reasoning, abilities rarely seen in earlier SLMs. This innovative method represents a potential shift in how we build speech AI. By focusing on descriptions of speech rather than explicit instruction-action pairs, DeSTA2 opens the door to more efficient, capable, and adaptable speech language models that can truly understand the richness and complexity of human speech. This work promises to accelerate the development of more natural and intuitive speech-based AI applications, from virtual assistants that understand nuance to advanced conversational AI that can truly engage in meaningful dialogue.