Audio-Aware Large Language Models as Judges for Speaking Styles
Authors: name: Cheng-Han Chiang, name: Xiaofei Wang, name: Chung-Ching Lin, name: Kevin Lin, name: Linjie Li, name: Radu Kopetz, name: Yao Qian, name: Zhendong Wang, name: Zhengyuan Yang, name: Hung-yi Lee, name: Lijuan Wang

Audio-aware large language models (ALLMs) can understand the textual and
non-textual information in the audio input. In this paper, we explore using
ALLMs as an automatic judge to assess the speaking styles of speeches. We use
ALLM judges to evaluate the speeches generated by SLMs on two tasks: voice
style instruction following and role-playing. The speaking style we consider
includes emotion, volume, speaking pace, word emphasis, pitch control, and
non-verbal elements. We use four spoken language models (SLMs) to complete the
two tasks and use humans and ALLMs to judge the SLMs' responses. We compare two
ALLM judges, GPT-4o-audio and Gemini-2.5-pro, with human evaluation results and
show that the agreement between Gemini and human judges is comparable to the
agreement between human evaluators. These promising results show that ALLMs can
be used as a judge to evaluate SLMs. Our results also reveal that current SLMs,
even GPT-4o-audio, still have room for improvement in controlling the speaking
style and generating natural dialogues.