Improving the performance of the speaker emotion recognition based on low dimension prosody features vector

Speaker emotion recognition is an important research issue as it finds lots of applications in human-robot interaction, computer-human interaction, etc. This work deals with the recognition of emotion of the speaker from speech utterance. For that features like pitch, log energy, zero crossing rate, and first three formant frequencies are used. Feature vectors are constructed using the 11 statistical parameters of each feature. The Artificial Neural Network (ANN) is chosen as a classifier owing to its universal function approximation capabilities. In ANN based classifier, the time required for training the network as well as for classification depends upon the dimension of feature vector. This work focused on development of a speaker emotion recognition system using prosody features as well as reduction of dimensionality of feature vectors. Here, principle component analysis (PCA) is used for feature vector dimensionality reduction. Emotional prosody speech and transcription from Linguistic Data Consortium (LDC) and Berlin emotional databases are considered for evaluating the performance of proposed approach for seven types of emotion recognition. The performance of the proposed method is compared with existing approaches and better performance is obtained with proposed method. From experimental results it is observed that 75.32% and 84.5% recognition rate is obtained for Berlin emotional database and LDC emotional speech database respectively.