雜訊對語音情緒辨識影響之研究 Investigation of Noise Effect to Emotion Recognition

ii In this thesis, the emotion recognition from noisy Mandarin speech is realized. This thesis proposed a useful method that was designed to improve recognition of emotions in Mandarin with different degree of noise. Recognition of emotions in speech is one of the challenges in the field of speech signal processing research. In particular, the selection of a feature set is arguably the most critical part when developing application of this kind. A proper choice of acoustic features can improve the performance of emotional Mandarin recognition system. To overcome the disturbance of noise, we made our efforts to develop a Mandarin emotion recognition method by means of combining a set of acoustic features using Weighted-Discrete-K-Nearest Neighborhood (Weighted-D-KNN) classifier. In the experiment, Mel-Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPCC), Log Frequency Power Coefficients (LFPC), and Relative Spectral PLP (Rasta-PLP) are selected as the features used in the recognition. Five emotions ar