A study of speech emotion recognition and its application to mobile services

In this paper, a speech emotion recognition agent for mobile communication service is proposed. The proposed system can recognize five emotional states - neutral, happiness, sadness, anger, and annoyance from the speech captured by a cellular phone in real time and then it calculates the degree of affection such as love, truthfulness, weariness, trick, friendship of the person who you are interesting to know through the mobile phone. In general, a speech acquired by a cellular phone contains noise due to the mobile network and environmental noise. Thus it can causes serious performance degradation due to the distortion in emotional features of the query speech. In order to alleviate the effect of these noises, we adopt a MA (Moving Average) filter which has relatively simple structure and low computational complexity. Then a feature optimization method is implemented to further improve and stabilize the system performance. For a practical application, we create an agent that can measure the degree of affection from the person who you want to know on the mobile phone. Two pattern classification methods, k-NN and SVM with probability estimates, are compared for estimating the degree of affection. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 72.5 % over five emotional states and it shows the feasibility of the agent for mobile communication services.