Speaker Identification System based on PLP Coefficients and Artificial Neural Network

Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves. Feature extraction for speech recognition is a subject of a major interest today; different features have been investigated in speech recognition systems. The perceptual linear predictive PLP: this technique uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum: (1) the critical-band spectral resolution, (2) the equal loudness curve, and (3) the intensity-loudness power law. This paper discusses the development of a speaker identification and phoneme classification system. In particular, we develop an artificial neural network: multilayer perceptron MLP using PLP coefficients of voice signal. The performance of the system has been tested in experiments using 14 Arabic phonemes, specifically the Arabic fricatives uttered by 4 Algerian native speakers. Our results demonstrates the efficiency of the PLP-MLP algorithm, a good recognition rate was obtained. KeywordsPLP, LPC, cepstrum, neural network, MLP.