Natural Language Dialog System Considering Speaker’s Emotion Calculated from Acoustic Features
With the development of Interactive Voice Response (IVR) systems , people can not only operate computer systems through task-oriented conversation but also enjoy non-task-oriented conversation with the computer. When an IVR system generates a response, it usually refers to just verbal information of the user’s utterance. However, when a person gloomily says “I’m fine,” people will respond not by saying “That’s wonderful” but “Really?” or “Are you OK?” because we can consider both verbal and non-verbal information such as tone of voice, facial expressions, gestures, and so on. In this article, we propose an intelligent IVR system that considers not only verbal but also non-verbal information. To estimate a speaker’s emotion (positive, negative, or neutral), 384 acoustic features extracted from the speaker’s utterance are utilized to machine learning (SVM). Artificial Intelligence Markup Language (AIML)-based response generating rules are expanded to be able to consider the speaker’s emotion. As a result of the experiment, subjects felt that the proposed dialog system was more likable, enjoyable, and did not give machine-like reactions.
KeywordsInteractive Voice Response System (IVR) Acoustic features Emotion Support Vector Machine (SVM) Artificial Intelligence Markup Language (AIML)
This research is supported by JSPS KAKENHI Grant Number 26330313 and the Center of Innovation Program from Japan Science and Technology Agency, JST.
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