In this work, we extend the instruction-tuned Llama-2 model with end-to-end
general-purpose speech processing and reasoning abilities while maintaining the
wide range of LLM capabilities, without using any carefully curated paired
data. The proposed model can utilize audio prompts as a replacement for text
and sustain a conversation. Such a model also has extended cross-modal
capabilities such as being able to perform speech question answering, speech
translation, and audio summarization amongst many other closed and open-domain
tasks. This is unlike prior approaches in speech, in which LLMs are extended to
handle audio for a limited number of pre-designated tasks. Experiments show
that our end-to-end approach is on par with or outperforms a cascaded system
(speech recognizer + LLM) in terms of modeling the response to a prompt.
Furthermore, unlike a cascade, our approach shows the ability to interchange
text and audio modalities and utilize the prior context in a conversation to
provide better results.

通过扩展 instruction-tuned Llama-2 模型的同时保持 LLM 的广泛能力范围，本研究提出了一种具备端到端通用语音处理和推理能力的模型。该模型可以使用音频提示代替文本进行对话，并且具备跨模态的能力，例如语音问答、语音翻译和音频摘要等。通过实验，我们证明了这种端到端的方法在建模回应时与或优于级联系统（语音识别器 + LLM），并且可以更好地利用对话中的先前上下文提供更好的结果。