Text-Dependent Multilingual Speaker Identification using Learning Vector Quantization and PSO-GA Hybrid Model

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract In this work a multilingual speaker identification system is proposed. The feature extraction techniques employed in system extract Mel frequency cepstral coefficient (MFCC), delta mel frequency cepstral coefficient (DMFCC) and format frequency. The feature selection is done using hybrid model of particle swarm optimizatiom (PSO) and Genetic Algorithm (GA). We have used Learning Vector Quantization (LVQ) artificial Neural Network classifiers. The speech database consists of 40 speakers (20 males+ 20 females) speech utterance. The speech utterance is recorded for a specific sentence in three different languages viz. “Now this time you go” (in English), “Adhuna Asmin Twam Gachh “(in Sanskrit), “Ab Iss Baar Tum Jao” (in Hindi). Total word for this purpose is 14 including 4 for Sanskrit and 5 Hindi and English. The average identification rate 79.99% is achieved when the Network is trained by LVQ and it shows 80.52% when LVQ is trained using hybrid PSO-GA model.