1 Dr. Mustafa Dhiaa Al-Hassani dr_mdhiaa77@yahoo.com Mustansiriyah University, Baghdad, Iraq Dr. Abdulkareem A. Kadhim abdulkareem.a@coie-nahrain.edu.iq Al-Nahrain University, Baghdad, Iraq Design A Text-Prompt Speaker Recognition System Using LPC-Derived Features Abstract: Humans are integrated closer to computers every day, and computers are taking over many services that used to be based on face-to- face contact between humans. This has prompted an active development in the field of biometric systems. The use of biometric information has been known widely for both person identification and security applications. The paper is concerned with the use of speaker features for protection against unauthorized access. A speaker recognition system for 6304 speech samples is presented that relies on LPC-derived features. A vocabulary of 46 speech samples is built for 10 speakers, where each authorized person is asked to utter every sample 10 times. Two different modes are considered in identifying individuals according to their speech samples. In the closed-set speaker identification, it is found that all tested LPC-derived features outperform the raw LPC coefficients and 84% to 97% identification rates are achieved. Applying the preprocessing steps to the speech signals (preemphasis, remove DC offset, frame blocking, overlapping, normalization and windowing) improve the representation of speech features, and up to 100% identification rate was obtained using weighted Linear Predictive Cepstral Coefficients (LPCC). In the open-set speaker verification mode of our proposed system model, the system selects randomly a pass phrase of 8-samples length from its database for each trial a speaker is presented to the system. Up to 213 text-prompt trials from 23-different speakers (authorized and unauthorized) are recorded (i.e., 1704 samples) in order to study the system behavior and to generate the optimal threshold in which the speakers are verified or not when compared to those training references of authorized speakers constructed in the first mode, where the best obtained speaker verification rate is greater than 99%. Keywords: Speaker Recognition, Speaker Identification, Speaker Verification, Biometric, Text-prompt, LPC-derived features, LSF. 1. Introduction As everyday life is getting more and more computerized, automated security systems are getting more and more important. Today most personal banking tasks can be performed over the Internet and soon they can also be performed on mobile devices such as cell phones and PDAs. The key task of an automated security system is to verify that the users are in fact those who claim to be [1]. Since the level of security breaches and transaction fraud increases, the need for highly secure identification and personal verification technologies is becoming apparent. Biometric-based solutions are able to provide confidential financial transactions and personal data privacy [2]. The need for biometrics can be found in federal, state and local governments, in the military, and in commercial applications [1, 3]. A biometric system is essentially a pattern recognition system that establishes the authenticity of a specific physiological or behavioral characteristic possessed by a user. They are typically based on some single biometric feature of humans, but several hybrid systems also exist [2, 4, 5, 1, 6]. Human voice can serve as a key for any security objects, and it is not easy to lose or forget it. This technique can be used to verify the identity claimed by people accessing systems; that is, it enables control of access to various services by voice [3, 7]. Speaker recognition has received for many years the attention of researchers working in the field of signal processing. This technology has been developed in such a way that it can be used in a number of applications, such as: voice dialing, banking over a telephone network, person authentication, remote access to computers, command and control systems, network security and protection, entry and access control systems, data access/information retrieval, Monitoring, … etc [8, 5, 9, 10, 11]. 2. Aim of the Work This work aims to build a speaker recognition (identification/verification) system that automatically authenticate a speaker's identity by his/her voice, according to a random text-prompt generated by the system, and then gives only the authorized persons a privilege or an access right to the facility that need to be protected from the intrusion of unauthorized persons. 3. The Proposed Speaker Recognition System Model In this section, several linear prediction based methods (LPC, PARCOR, LAR, ASRC, LPCC, and LSF) are tested for text- dependent speaker recognition system in a closed-set mode. The open-set speaker verification mode is also investigated, which involves speaker’s verification according to a randomly text- prompt sentence generated by the system. The block diagram for the proposed speaker recognition system model, shown in Fig. (1), illustrates that the input speech is passed through six preprocessing operations (preemphasis, remove DC offset, frame blocking, overlapping, normalization and windowing) prior to feature extraction phase. If the match is lower than certain threshold, then the identity claims is verified "Accepted", otherwise, the speaker is "Rejected" [1].