Speech Emotion Recognition Using Confidence Level for Emotional Interaction Robot
نویسندگان
چکیده
منابع مشابه
Speech emotion recognition in emotional feedback for Human-Robot Interaction
For robots to plan their actions autonomously and interact with people, recognizing human emotions is crucial. For most humans nonverbal cues such as pitch, loudness, spectrum, speech rate are efficient carriers of emotions. The features of the sound of a spoken voice probably contains crucial information on the emotional state of the speaker, within this framework, a machine might use such pro...
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Speech emotion recognition is currently an active research subject and has attracted extensive interest in the science community due to its vital application to human‐robot interaction. Most speech emotion recognition systems employ high‐dimensional speech features, indicating human emotion expression, to improve emotion recognition performance. To effectively reduce t...
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speech emotion can add more information to speech in comparison to available textual information. however, it will also lead to some problems in speech recognition process. in a previous study, we depicted the substantial changes of speech parameters caused by speech emotion. therefore, in order to improve emotional speech recognition rate, in a first step, the effects of emotion on speech par...
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Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...
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We present a method to generate effective confirmation and guidance using concept-level confidence measures (CM) derived from speech recognizer output in order to handle speech recognition errors. We define two conceptlevel CM, which are on content-words and on semanticattributes, using 10-best outputs of the speech recognizer and parsing with phrase-level grammars. Content-word CM is useful fo...
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ژورنال
عنوان ژورنال: Journal of Korean Institute of Intelligent Systems
سال: 2009
ISSN: 1976-9172
DOI: 10.5391/jkiis.2009.19.6.755