Emotion Recognition Using Bahasa Malaysia Natural Speech

In recent years, the technology of emotion speech recognition has gradually become more important to the industries. This is proven by the integration of this system into many applications such as the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies and so on. Generally, speech emotion recognition (SER) is developed to help humans to understand and retrieve desired emotions. In this research, the analysis of using Bahasa Malaysia Language for three basic emotions of happy, sad and angry was analyzed. A total of 30 male and 30 female audio recordings were collected. Mel-frequency cepstral coefficient, chroma and mel spectrogram features were extracted. Feature dimensions were reduced using forward, backward and exhaustive selection methods before classification. Classification was performed using K-nearest neighbors, Support Vector Machine and Random Forest. The analysis demonstrated 78% accuracy for male speech and 78% for female speech.