Emotion Recognition and Evaluation of Mandarin Speech Us：情感识别和评价汉语语音的我们精品.pdfVIP

Emotion Recognition and Evaluation of Mandarin Speech Using Weighted D-KNN Classification Tsang-Long Pao, Yu-Te Chen, Jun-Heng Yeh, Yuan-Hao Chang Department of Computer Science and Engineering, Tatung University, Taipei tlpao@ttu.edu.tw, d8906005@mail.ttu.edu.tw, g9206026@ms2.ttu.edu.tw Abstract. 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 acoustic features are Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC). The results reveal that the highest recognition rate is 79.55% obtained with weighted D-KNN optimized based on Fibonacci series. Besides, we design an emotion radar chart which can present the intensity of each emotion in our emotion evaluation system. Based on our emotion evaluation system, we implement a computer-assisted speech training system for training the hearing-impaired people to speak more naturally. 1 Introduction Recognizing emotions from speech has gained increased attention recently, because automatic emotion recognition can help people to develop and design many applications about human-machine communication. In emotion recognition, collecting corpus and selecting the suitable features and classification algorithms are the two most difficult problems. Language is the most basic and main tool for the human to communicate thoughts, convey messages and express aspiration. For the hearing-normal people, the process of speak learning