Hybrid approach to speech recognition in multi-speaker environment

Abstract

Recognition of voice, in a multi-speaker environment involves speech separation, speech feature extraction and speech feature matching. Traditionally, Vector Quantization is one of the algorithms used for speaker recognition. However, the effectiveness of this approach is not well appreciated in case of noisy or multi-speaker environment. This thesis describes a thorough study of the speech separation and speaker recognition process and a couple of benchmark algorithms have been analysed. Usage of Independent Component Analysis (ICA) in speech separation process has been studied in minute details. The accuracy of the traditional techniques was tested by simulation in MATLAB. Later, a hybrid approach for speech separation and speaker recognition in a multi-speaker environment has been proposed. Test results of a series of experiments that attempt to improve speaker recognition accuracy for multi-speaker environment by using the proposed hybrid approach are presented. Speaker recognition results obtained by this approach are also compared with the results obtained using a more conventional direct approach and the usefulness of the hybrid approach is established.