On decoder-only architecture for speech-to-text and large language model
  integration
Authors: name: Jian Wu, name: Yashesh Gaur, name: Zhuo Chen, name: Long Zhou, name: Yimeng Zhu, name: Tianrui Wang, name: Jinyu Li, name: Shujie Liu, name: Bo Ren, name: Linquan Liu, name: Yu Wu

Large language models (LLMs) have achieved remarkable success in the field of
natural language processing, enabling better human-computer interaction using
natural language. However, the seamless integration of speech signals into LLMs
has not been explored well. The "decoder-only" architecture has also not been
well studied for speech processing tasks. In this research, we introduce
Speech-LLaMA, a novel approach that effectively incorporates acoustic
information into text-based large language models. Our method leverages
Connectionist Temporal Classification and a simple audio encoder to map the
compressed acoustic features to the continuous semantic space of the LLM. In
addition, we further probe the decoder-only architecture for speech-to-text
tasks by training a smaller scale randomly initialized speech-LLaMA model from
speech-text paired data alone. We conduct experiments on multilingual
speech-to-text translation tasks and demonstrate a significant improvement over
strong baselines, highlighting the potential advantages of decoder-only models
for speech-to-text conversion.