Addressee Estimation is the ability to understand to whom a person is
talking, a skill essential for social robots to interact smoothly with humans.
In this sense, it is one of the problems that must be tackled to develop
effective conversational agents in multi-party and unstructured scenarios. As
humans, one of the channels that mainly lead us to such estimation is the
non-verbal behavior of speakers: first of all, their gaze and body pose.
Inspired by human perceptual skills, in the present work, a deep-learning model
for Addressee Estimation relying on these two non-verbal features is designed,
trained, and deployed on an iCub robot. The study presents the procedure of
such implementation and the performance of the model deployed in real-time
human-robot interaction compared to previous tests on the dataset used for the
training.

研究通过深度学习模型设计了一个基于非语言特征的地址估计系统，用于实现社交机器人与人类的平滑互动。该模型在 iCub 机器人上进行实时人机交互测试，并与训练数据集上的先前测试结果进行了比较。

实时收信人估计：在 iCub 机器人上部署一个深度学习模型

Real-time Addressee Estimation: Deployment of a Deep-Learning Model on  the iCub Robot

In this paper, we introduce the DiffuseStyleGesture+, our solution for the
Generation and Evaluation of Non-verbal Behavior for Embodied Agents (GENEA)
Challenge 2023, which aims to foster the development of realistic, automated
systems for generating conversational gestures. Participants are provided with
a pre-processed dataset and their systems are evaluated through crowdsourced
scoring. Our proposed model, DiffuseStyleGesture+, leverages a diffusion model
to generate gestures automatically. It incorporates a variety of modalities,
including audio, text, speaker ID, and seed gestures. These diverse modalities
are mapped to a hidden space and processed by a modified diffusion model to
produce the corresponding gesture for a given speech input. Upon evaluation,
the DiffuseStyleGesture+ demonstrated performance on par with the top-tier
models in the challenge, showing no significant differences with those models
in human-likeness, appropriateness for the interlocutor, and achieving
competitive performance with the best model on appropriateness for agent
speech. This indicates that our model is competitive and effective in
generating realistic and appropriate gestures for given speech. The code,
pre-trained models, and demos are available at
this https URL

通过 DiffuseStyleGesture + 模型，本论文介绍了一种用于生成对话手势的非语言行为的生成和评估的解决方案。该模型利用扩散模型自动生成手势，通过音频、文本、说话者 ID 和种子手势等多种模态对隐藏空间进行映射和处理，在 GENEA Challenge 中展示了与顶尖模型相当的表现，完成了生成逼真且适合特定语音的手势的目标。

GENEA Challenge 2023 的 DiffuseStyleGesture + 参赛方案

The DiffuseStyleGesture+ entry to the GENEA Challenge 2023

Socially Interactive Agents (SIAs) are physical or virtual embodied agents
that display similar behavior as human multimodal behavior. Modeling SIAs'
non-verbal behavior, such as speech and facial gestures, has always been a
challenging task, given that a SIA can take the role of a speaker or a
listener. A SIA must emit appropriate behavior adapted to its own speech, its
previous behaviors (intra-personal), and the User's behaviors (inter-personal)
for both roles. We propose AMII, a novel approach to synthesize adaptive facial
gestures for SIAs while interacting with Users and acting interchangeably as a
speaker or as a listener. AMII is characterized by modality memory encoding
schema - where modality corresponds to either speech or facial gestures - and
makes use of attention mechanisms to capture the intra-personal and
inter-personal relationships. We validate our approach by conducting objective
evaluations and comparing it with the state-of-the-art approaches.

提出 AMII 方法，利用注意力机制综合非言语行为以适应 Socially Interactive Agents 的不同角色。