ChatGPT 和 Whisper 是否使人形机器人更具亲和力？
Does ChatGPT and Whisper Make Humanoid Robots More Relatable?
AI总结：
本文比较了不同ASR API，将Whisper ASR和ChatGPT集成到Pepper机器人中，经15名用户测试，证明该系统显著提升了人机沟通效果与亲和力。
摘要：
人形机器人被设计为能够与人类建立亲和关系，以应用于客户支持和帮助台服务等场景。然而，许多此类系统（包括 Softbank 的 Pepper）由于无法与人类有效沟通而存在不足。大型语言模型（LLMs）的出现展现出解决人形机器人沟通障碍的潜力。本文概述了不同自动语音识别（ASR）API 的比较，将 Whisper ASR 和 ChatGPT 集成到 Pepper 机器人中，并对由 15 名人类用户测试的该系统（Pepper-GPT）进行了评估。比较结果表明，与 Google ASR 和 Google Cloud ASR 相比，Whisper ASR 表现最佳，其平均词错率（1.716%）和处理时间（2.639 s）均为最低。参与者的可用性调查显示，60% 的参与者认为 Pepper-GPT 的性能为“优秀”，其余参与者在后续实验中将该系统评为“良好”。结果表明，尽管仍需克服一些问题（如机器人的多语言能力和面部跟踪能力），但用户对该系统总体反应积极，感觉像在与真实人类交谈。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
