Speaker Identification: A Hybrid Approach Using Neural Networks and Wavelet Transform
Muzhir Shaban Al-Ani, Thabit Sultan Mohammed, Karim Mohammed Aljebory · Journal of Computer Science · 2007
Abstract: In speaker identification systems, a database is constructed from the speech samples of known speakers. The approach implemented in this paper is hybrid, where the wavelet transform and neural networks are used together to form a system with improved performance. Features are extracted by applying a discrete wavelet transform (DWT), while a neural network (NN) is used for formulating the system database and for handling the task of decision making. The neural network is trained using inputs, which are the feature vectors. A criteria depends on both false acceptance ratio (FAR) and false rejection ratio (FRR) is used to evaluate the system performance. For experimenting the proposed system, a set of 25 randomly aged male and female speakers was used. Results of admitting the members of this set to a secure system were computed and presented. The evaluation criteria parameters obtained are; FAR=14.5 % and FRR=24.5% Key words: Speaker identification, speaker recognition, discrete wavelet transform, multi-valued neural networks