Yu-Te Chen

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Emotions play a significant role in decision-making, healthy, perception, human interaction and human intelligence. Automatic recognition of emotion in speech is very desirable because it adds to the human- computer interaction and becomes an important research area in the last years. However, to the best of our knowledge, no works have focused on automatic(More)
In this paper, we proposed a weighted discrete K-nearest neighbor (weighted D-KNN) classification algorithm for detecting and evaluating emotion from Mandarin speech. In the experiments of the emotion recognition, Mandarin emotional speech database used contains five basic emotions, including anger, happiness, sadness, boredom and neutral, and the extracted(More)
Automatic emotional speech recognition system can be characterized by the selected features, the investigated emotional categories, the methods to collect speech utterances, the languages, and the type of classifier used in the experiments. Until now, several classifiers are adopted independently and tested on numerous emotional speech corpora but no any(More)
In this paper, a Mandarin speech based emotion classification method is presented. Five primary human emotions including anger, boredom, happiness, neutral and sadness are investigated. In emotion classification of speech signals, the conventional features are statistics of fundamental frequency, loudness, duration and voice quality. However, the(More)