Emotion Recognition and Evaluation of Mandarin Speech Using Weighted D-KNN Classification

Emotion Recognition and Evaluation of Mandarin Speech Using Weighted D-KNN Classification

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.