A tutorial on the design and development of automatic speaker-recognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic speaker-recognition systems are shown and design tradeoffs are discussed. A new automatic speaker-recognition system is given, which performs with 98.9% correct identification. The performances of various systems are compared. Keywords—Access control, authentication, biomedical measurements, biomedical signal processing, biomedical transducers, biometric, communication system security, computer network security, computer security, corpus, data bases, identification of persons, public safety, site security monitoring, speaker recognition, speech processing, verification. The paper focuses on the applications of speaker recognition in facilities and network access control. Speaker recognition encompasses verification and identification. Automatic speaker verification (ASV) is the use of a machine to verify a person's claimed identity from his voice. Automatic speaker identification (ASI) determines who a person is without a priori identity claim. Speaker verification is defined as deciding if a speaker is whom he claims to be. In speaker verification, a person makes an identity claim, such as by entering an employee number or presenting a smart card. The claimant speaks a phrase into a microphone, which is analyzed by a verification system to accept or reject the user's identity claim. A typical ASV setup involves a claimant presenting an encrypted smart card and speaking a prompted phrase. There is a tradeoff between accuracy and test-session duration. Users must enroll in the system, generating and storing voice models. Factors such as ambient noise and human error can contribute to verification and identification errors. Speaker recognition is a performance biometric, where a task is performed to be recognized. Speaker recognition systems can be robust against noise and channel variations. The paper discusses the problem formulation, generic speaker verification, and previous work in speaker recognition. It covers speech processing, feature selection, pattern matching, and classification. A simple but effective speaker-recognition algorithm is presented, with experimental results showing high accuracy. The paper also discusses the use of features such as divergence shape and Bhattacharyya distance. It highlights the importance of accurate feature selection and the challenges of speaker verification in different environments. The paper concludes with a summary of the key points and the potential applications of speaker recognition in various fields.A tutorial on the design and development of automatic speaker-recognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic speaker-recognition systems are shown and design tradeoffs are discussed. A new automatic speaker-recognition system is given, which performs with 98.9% correct identification. The performances of various systems are compared. Keywords—Access control, authentication, biomedical measurements, biomedical signal processing, biomedical transducers, biometric, communication system security, computer network security, computer security, corpus, data bases, identification of persons, public safety, site security monitoring, speaker recognition, speech processing, verification. The paper focuses on the applications of speaker recognition in facilities and network access control. Speaker recognition encompasses verification and identification. Automatic speaker verification (ASV) is the use of a machine to verify a person's claimed identity from his voice. Automatic speaker identification (ASI) determines who a person is without a priori identity claim. Speaker verification is defined as deciding if a speaker is whom he claims to be. In speaker verification, a person makes an identity claim, such as by entering an employee number or presenting a smart card. The claimant speaks a phrase into a microphone, which is analyzed by a verification system to accept or reject the user's identity claim. A typical ASV setup involves a claimant presenting an encrypted smart card and speaking a prompted phrase. There is a tradeoff between accuracy and test-session duration. Users must enroll in the system, generating and storing voice models. Factors such as ambient noise and human error can contribute to verification and identification errors. Speaker recognition is a performance biometric, where a task is performed to be recognized. Speaker recognition systems can be robust against noise and channel variations. The paper discusses the problem formulation, generic speaker verification, and previous work in speaker recognition. It covers speech processing, feature selection, pattern matching, and classification. A simple but effective speaker-recognition algorithm is presented, with experimental results showing high accuracy. The paper also discusses the use of features such as divergence shape and Bhattacharyya distance. It highlights the importance of accurate feature selection and the challenges of speaker verification in different environments. The paper concludes with a summary of the key points and the potential applications of speaker recognition in various fields.