Analysis of windowing techniques for speech emotion recognition

Conventionally, the spectral features are derived from the DFT spectrum using the Hamming window. The spectral leakage is reduced by windowing but the variance of the spectral estimate is high. Multitaper method emphasizes on using multiple windows and frequency domain averaging. In this paper we study the impact of introduction of multitapering on the performance of Speech Emotion Recognition. Various spectral features including MFCCs are taken into consideration while the classifier used is Support Vector Machine (SVM). For the spectral features, in case of multitapering an improvement of upto 2% was found as compared to traditional Hamming window when tested on Berlin database. Impact of variable frame size, different windows and variable taper number is also studied.