Vector Quantization Approach to Classification of Stressed Speech

Abstract

In this work, a stressed (emotional) speech recognition system is designed based on Vector-Quantization (VQ) approach using Generalized Lloyd algorithm. Frequency, Amplitude and Phase features are extracted from the Sinusoidal model of speech and these parameters are used for characterization of stressed speech. The stressed conditions considered in this study are anger, happiness, compassion and neutral. Data for analysis and classification of stressed speech are recorded from thirty speakers; each speaker uttered five statements for each emotion in two different languages, English and Telugu (an Indian language). The features of stressed speech signals are compared with the features of neutral speech signals. From the results, it has been observed that the sinusoidal model features can be used successfully for classification of stressed speech. A total of 320 stressed speech data files are used for training and 280 stressed speech data files are used for testing. The performance of the VQ classifier with sinusoidal model features has been tested under normal and noisy conditions. A maximum recognition of 92.8% is achieved with frequency features as the input to the VQ classifier.