A Voice Identification System using Hidden Markov Model

Background/Objectives: Voice Identification System refers to a system which comprises of hardware, software and it is used to identify voice for several applications. The aim of the research is to develop a small scale system that incorporate both speaker recognition and speech recognition and can show specific visual information to a user. Methods: To this end, we have developed a system based on the technique of Hidden Markov Model. The Hidden Markov Model is a stochastic approach which models the algorithm as a double stochastic process in which the observed data is thought to be the result of having passed a hidden process through second process. Both processes are characterized only through one that is observed. A database of voice information is created. To extract features from voice signals, Mel-Frequency Cepstral Coefficients (MFCC) technique has been applied producing a set of feature vectors. Subsequently, the system uses The Vector Quantization (VQ) for features training and classification. Findings: The designed system has been tested with multiple speakers as reference. Speech recognition based on Hidden Markov Model is achieved successfully for the conversion of speech to text. In this proposed research, speech recognition is achieved with accuracy about 90%. Applications: The system has potential to be used in music industry, crime investigation, personal assistant and in hi-tech devices.