Development of Speaker Recognition Model for Forensic Application

Abstract

Voice is a natural communication tool humans use to convey meanings, ideas, opinions, etc. In particular, quotvoicequot pertains to any sound generated through the vibration of vocal folds when air pressure is from the lungs. It encompasses various characteristics of the speaker, such as ethnicity, age, gender, and emotions. The utilisation of biometrics, particularly voice recognition, has gained popularity in the realm of security. Beyond facial recognition, distinct features like the retina, iris, and voice can be employed to distinguish individuals. Biometrics can be broadly classified as either physiological or behavioural. Physiological biometrics involve features like the face, finger-print, and iris, while behavioural biometrics encompass voice, keystroke, and signature. Among these, voice recognition is one of the most valuable technologies due to its user-friendly nature, widespread acceptance, and cost-effectiveness. Speaker recognition research has been ongoing for several decades, experiencing significant advancements in signal processing, algorithms, architecture, and hardware. Specifically, voice refers to any sound produced by vocal fold vibration when air from the lungs is under pressure. It carries various traits of the speaker, including ethnicity, age, gender, and emotions. The use of biometrics, including voice recognition, has gained popularity in the field of security. In addition to facial recognition, other unique features such as the retina, iris, and voice can also be used to distinguish individuals. Biometrics can be categorised as physiological and behavioural. Physiological biometrics include features like the face, fingerprint, and iris, while behavioral biometrics include voice, keystroke, and signature. Voice recognition is considered one of the most useful technologies. It is easy to use and implement, widely accepted by users, and cost-effective. Research in speaker recognition has been conducted for several decades and has significantly evolved with advancements in signal processing