The nature of statistical learning theory
The nature of statistical learning theory
Digital Speech; Coding for Low Bit Rate Communication Systems
Digital Speech; Coding for Low Bit Rate Communication Systems
Recognition of Affective Communicative Intent in Robot-Directed Speech
Autonomous Robots
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in humanComputer interaction
Real-Time Spoken Affect Classification and Its Application in Call-Centres
ICITA '05 Proceedings of the Third International Conference on Information Technology and Applications (ICITA'05) Volume 2 - Volume 02
Ensemble methods for spoken emotion recognition in call-centres
Speech Communication
Applying an analysis of acted vocal emotions to improve the simulation of synthetic speech
Computer Speech and Language
Comparison of Several Classifiers for Emotion Recognition from Noisy Mandarin Speech
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 01
Feature Combination for Better Differentiating Anger from Neutral in Mandarin Emotional Speech
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Expert Systems with Applications: An International Journal
Acoustic feature selection for automatic emotion recognition from speech
Information Processing and Management: an International Journal
Emotion recognition from speech signals using new harmony features
Signal Processing
Computers in Human Behavior
Multimodal information fusion application to human emotion recognition from face and speech
Multimedia Tools and Applications
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Recognition of emotion in speech has recently matured to one of the key disciplines in speech analysis serving next generation human-machine interaction and communication. However, compared to automatic speech recognition, that emotion recognition from an isolated word or a phrase is inappropriate for conversation. Because a complete emotional expression may stride across several sentences, and may fetch-up on any word in dialogue. In this paper, we present a segment-based emotion recognition approach to continuous Mandarin Chinese speech. In this proposed approach, the unit for recognition is not a phrase or a sentence but an emotional expression in dialogue. To that end, the following procedures are presented: First, we evaluate the performance of several classifiers in short sentence speech emotion recognition architectures. The results of the experiments show that the WD-KNN classifier achieves the best accuracy for the 5-class emotion recognition what among the five classification techniques. We then implemented a continuous Mandarin Chinese speech emotion recognition system with an emotion radar chart which is based on WD-KNN; this system can represent the intensity of each emotion component in speech. This proposed approach shows how emotions can be recognized by speech signals, and in turn how emotional states can be visualized.