论文标题
使用ICUB类人机器人的自我监督的强化学习来替代说话者本地化
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot
论文作者
论文摘要
将来，机器人将越来越多地与人类互动，并且必须自然有效地进行沟通。自动语音识别系统（ASR）将在建立自然互动并使机器人更好的伴侣中发挥重要作用。人类在嘈杂的环境中表现出色的语音识别，并能够过滤掉噪声。看着一个人的脸是人类在这种嘈杂环境中过滤语音时所依赖的机制之一。拥有一个可以看待演讲者的机器人可以使ASR的性能受益。为此，我们提出了一个由人类的早期开发启发的基于自我监督的强化学习框架，允许机器人自主创建一个数据集，该数据集随后被用来学习通过深度学习网络来局部化扬声器。
In the future robots will interact more and more with humans and will have to communicate naturally and efficiently. Automatic speech recognition systems (ASR) will play an important role in creating natural interactions and making robots better companions. Humans excel in speech recognition in noisy environments and are able to filter out noise. Looking at a person's face is one of the mechanisms that humans rely on when it comes to filtering speech in such noisy environments. Having a robot that can look toward a speaker could benefit ASR performance in challenging environments. To this aims, we propose a self-supervised reinforcement learning-based framework inspired by the early development of humans to allow the robot to autonomously create a dataset that is later used to learn to localize speakers with a deep learning network.
