Social robots aim to establish long-term bonds with humans through engaging
conversation. However, traditional conversational approaches, reliant on
scripted interactions, often fall short in maintaining engaging conversations.
This paper addresses this limitation by integrating large language models
(LLMs) into social robots to achieve more dynamic and expressive conversations.
We introduce a fully-automated conversation system that leverages LLMs to
generate robot responses with expressive behaviors, congruent with the robot's
personality. We incorporate robot behavior with two modalities: 1) a
text-to-speech (TTS) engine capable of various delivery styles, and 2) a
library of physical actions for the robot. We develop a custom,
state-of-the-art emotion recognition model to dynamically select the robot's
tone of voice and utilize emojis from LLM output as cues for generating robot
actions. A demo of our system is available here. To illuminate design and
implementation issues, we conduct a pilot study where volunteers chat with a
social robot using our proposed system, and we analyze their feedback,
conducting a rigorous error analysis of chat transcripts. Feedback was
overwhelmingly positive, with participants commenting on the robot's empathy,
helpfulness, naturalness, and entertainment. Most negative feedback was due to
automatic speech recognition (ASR) errors which had limited impact on
conversations. However, we observed a small class of errors, such as the LLM
repeating itself or hallucinating fictitious information and human responses,
that have the potential to derail conversations, raising important issues for
LLM application.

通过将大型语言模型（LLMs）整合到社交机器人中，本研究旨在解决传统基于脚本互动的对话方法在维持有趣的对话方面存在的局限性。我们引入了一个完全自动化的对话系统，利用 LLMs 生成具有有表情的机器人回应，与机器人的个性一致。我们结合两种模式的机器人行为：1）具有多种表现风格的文本转语音（TTS）引擎，和 2）用于机器人的动作库。我们开发了一个定制的最先进的情感识别模型，用于动态选择机器人的语调，并利用 LLM 输出中的表情符号作为生成机器人动作的线索。通过一个志愿者与社交机器人进行对话的试验，我们研究和分析了他们的反馈，对聊天记录进行了严格的错误分析，以阐明设计和实现问题。反馈普遍积极，参与者评论机器人具有共情力、乐于助人、自然和娱乐性。最负面的反馈是由于自动语音识别（ASR）错误，这对对话的影响有限。然而，我们观察到了一类较小的错误，如 LLM 的自我重复或虚构信息和人类回答的产生，这可能会破坏对话，引发了 LLM 应用中的重要问题。

使用 LLMs 生成饶有表情的机器人行为与桌面机器人 Haru 的对话

Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in  Conversations with the Tabletop Robot Haru

Automatic depression detection has attracted increasing amount of attention
but remains a challenging task. Psychological research suggests that depressive
mood is closely related with emotion expression and perception, which motivates
the investigation of whether knowledge of emotion recognition can be
transferred for depression detection. This paper uses pretrained features
extracted from the emotion recognition model for depression detection, further
fuses emotion modality with audio and text to form multimodal depression
detection. The proposed emotion transfer improves depression detection
performance on DAIC-WOZ as well as increases the training stability. The
analysis of how the emotion expressed by depressed individuals is further
perceived provides clues for further understanding of the relationship between
depression and emotion.

通过使用预训练的情感识别模型进行特征提取来实现自动抑郁症检测，结合音频和文本的情感信息，提出了一种情感传递方法来增加训练的稳定性和检测性能，在进一步理解抑郁症与情感关系方面提供了线索。