Alle Publikationen
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2018
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(2018) : Artificial Empathy in Social Robots: An analysis of Emotions in Speech: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): Nanjing, China: IEEE Robotics & Automation Society, S. 632-637
DOI: https://doi.org/10.1109/ROMAN.2018.8525652 Abstract: Artificial speech developed using speech synthesizers has been used as the voice for robots in Human Robot Interaction (HRI). As humans anthropomorphize robots, an empathetically interacting robot is expected to increase the level of acceptance of social robots. Here, a human perception experiment evaluates whether human subjects perceive empathy in robot speech. For this experiment, empathy is expressed only by adding appropriate emotions to the words in speech. Also, humans’ preferences for a robot interacting with empathetic speech versus a standard robotic voice are also assessed. The results show that humans are able to perceive empathy and emotions in robot speech, and prefer it over the standard robotic voice. It is important for the emotions in empathetic speech to be consistent with the language content of what is being said, and with the human users’ emotional state. Analyzing emotions in empathetic speech using valence-arousal model has revealed the importance of secondary emotions in developing empathetically speaking social robots.
Keywords: Angemessen(heit) (von Technik), Anthropomorphism, appropriate emotions, artificial empathy, artificial speech, control engineering computing, emotion recognition, empathetic speech, empathetically interacting robot, human perception experiment, Human robot interaction, human subjects, human users, human-robot interaction, Humans, ieee xplore, Medical services, robot interacting, Robot sensing systems, robot speech, Robots, service robot, social robots, speech synthesis, speech synthesizers, standard robotic voice, standards, Task Analysis 2015
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(2015): A cloud robotics approach towards dialogue-oriented robot speech. In: Advanced Robotics 29 (7), S. 449-456. DOI: 10.1080/01691864.2015.1009164
Abstract: Robot utterances generally sound monotonous, unnatural and unfriendly because their Text-to-Speech systems are not optimized for communication but for text reading. Here, we present a non-monologue speech synthesis for robots. The key novelty lies in speech synthesis based on Hidden Markov models (HMMs) using a non-monologue corpus: we collected a speech corpus in a non-monologue style in which two professional voice talents read scripted dialogues, and HMMs were then trained with the corpus and used for speech synthesis. We conducted experiments in which the proposed method was evaluated by 24 subjects in three scenarios: text reading, dialogue and domestic service robot (DSR) scenarios. In the DSR scenario, we used a physical robot and compared our proposed method with a baseline method using the standard Mean Opinion Score criterion. Our experimental results showed that our proposed method's performance was (1) at the same level as the baseline method in the text-reading scenario and (2) exceeded it in the DSR scenario. We deployed our proposed system as a cloud-based speech synthesis service so that it can be used without any cost.
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