A Hidden Markov Model based speaker identification system using mobile phone database of North Atlantic Treaty Organization words

• Published in: The Journal of the Acoustical Society of America 2013-05, Vol.133 (5_Supplement), p.3247-3247
• Main Authors: Agrawal, Shyam S., Bansal, Shweta, Pandey, Dipti
• Format: Article
• Language: English
• ISSN: 0001-4966; 1520-8524
• DOI: 10.1121/1.4805213

This paper describes results of an experiment to conduct text independent speaker identification of large number of speakers (about 100) using a standard vocabulary of about 23 NATO words—such as Alfa, Bravo, etc. These words in isolation were spoken in a sound treated room by Hindi natives having very good education in English (both males and females) and recorded by a three channel data recording system—the cardioid microphone, electret condenser microphone, and a NOKIA mobile telephone. The pre-processed digitized database of isolated words was further processed to determine 39 MFCC's and their derivatives and used to build an HMM model for each speaker based on all the words. The HMM model was trained using an HTK tool kit to generate the model parameters and tested using Viterbi algorithm. The identification of speakers was done in a closed set manner, based on comparison of each NATO word in the model. In addition to correct identification, false acceptance and false rejection scores were also found. The results show varying performance due to variations in channels, male/female speakers. The overall identification scores vary between 60% and 70%. The paper gives detailed analysis of results.