Humanoid robots are designed to be relatable to humans for applications such as customer support and helpdesk services. However, many such systems, including Softbank's Pepper, fall short because they fail to communicate effectively with humans. The advent of Large Language Models (LLMs) shows the potential to solve the communication barrier for humanoid robotics. This paper outlines the comparison of different Automatic Speech Recognition (ASR) APIs, the integration of Whisper ASR and ChatGPT with the Pepper robot and the evaluation of the system (Pepper-GPT) tested by 15 human users. The comparison result shows that, compared to the Google ASR and Google Cloud ASR, the Whisper ASR performed best as its average Word Error Rate (1.716%) and processing time (2.639 s) are both the lowest. The participants' usability investigations show that 60% of the participants thought the performance of the Pepper-GPT was "excellent", while the rest rated this system as "good" in the subsequent experiments. It is proved that while some problems still need to be overcome, such as the robot's multilingual ability and facial tracking capacity, users generally responded positively to the system, feeling like talking to an actual human.

翻译：类人机器人被设计用于客户支持与客服等需要与人类建立亲切感的应用场景。然而，包括软银的Pepper在内的许多此类系统，由于未能与人类有效沟通而表现欠佳。大型语言模型（LLMs）的出现展现了解决类人机器人沟通障碍的潜力。本文概述了不同自动语音识别（ASR）应用程序编程接口（API）的对比、Whisper ASR与ChatGPT在Pepper机器人上的集成，以及由15名人类用户测试的系统（Pepper-GPT）评估结果。对比结果表明，与Google ASR和Google Cloud ASR相比，Whisper ASR表现最佳，其平均词错误率（1.716%）和处理时间（2.639秒）均为最低。参与者的可用性调查显示，60%的参与者认为Pepper-GPT的表现“优秀”，其余参与者则在后续实验中将其评为“良好”。研究证明，尽管仍需克服机器人多语言能力与面部追踪能力等问题，但用户总体对该系统反响积极，感觉仿佛在与真人对话。