A Review of Voice-Base Person Identification: State-of-the-Art

Automated person identification and authentication systems are useful for national security, integrity of electoral processes, prevention of cybercrimes and many access control applications. This is a critical component of information and communication technology which is central to national development. The use of biometrics systems in identification is fast replacing traditional methods such as use of names, personal identification numbers codes, password, etc., since nature bestow individuals with distinct personal imprints and signatures. Different measures have been put in place for person identification, ranging from face, to fingerprint and so on. This paper highlights the key approaches and schemes developed in the last five decades for voice-based person identification systems. Voice-base recognition system has gained interest due to its non-intrusive technique of data acquisition and its increasing method of continually studying and adapting to the person’s changes. Information on the benefits and challenges of various biometric systems are also presented in this paper. The present and prominent voice-based recognition methods are discussed. It was observed that these systems application areas have covered intelligent monitoring, surveillance, population management, election forensics, immigration and border control

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Decision fusion for multi-modal person authentication.

Hui Pak Sum Henry.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves [147]-152).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1. --- Objectives --- p.4Chapter 1.2. --- Thesis Outline --- p.5Chapter 2. --- Background --- p.6Chapter 2.1. --- User Authentication Systems --- p.6Chapter 2.2. --- Biometric Authentication --- p.9Chapter 2.2.1. --- Speaker Verification System --- p.9Chapter 2.2.2. --- Face Verification System --- p.10Chapter 2.2.3. --- Fingerprint Verification System --- p.11Chapter 2.3. --- Verbal Information Verification (VIV) --- p.12Chapter 2.4. --- Combining SV and VIV --- p.15Chapter 2.5. --- Biometric Decision Fusion Techniques --- p.17Chapter 2.6. --- Fuzzy Logic --- p.20Chapter 2.6.1. --- Fuzzy Membership Function and Fuzzy Set --- p.21Chapter 2.6.2. --- Fuzzy Operators --- p.22Chapter 2.6.3. --- Fuzzy Rules --- p.22Chapter 2.6.4. --- Defuzzification --- p.23Chapter 2.6.5. --- Advantage of Using Fuzzy Logic in Biometric Fusion --- p.23Chapter 2.7. --- Chapter Summary --- p.25Chapter 3. --- Experimental Data --- p.26Chapter 3.1. --- Data for Multi-biometric Fusion --- p.26Chapter 3.1.1. --- Speech Utterances --- p.30Chapter 3.1.2. --- Face Movement Video Frames --- p.31Chapter 3.1.3. --- Fingerprint Images --- p.32Chapter 3.2. --- Data for Speech Authentication Fusion --- p.33Chapter 3.2.1. --- SV Training Data for Speaker Model --- p.34Chapter 3.2.2. --- VIV Training Data for Speaker Independent Model --- p.34Chapter 3.2.3. --- Validation Data --- p.34Chapter 3.3. --- Chapter Summary --- p.36Chapter 4. --- Authentication Modules --- p.37Chapter 4.1. --- Biometric Authentication --- p.38Chapter 4.1.1. --- Speaker Verification --- p.38Chapter 4.1.2. --- Face Verification --- p.38Chapter 4.1.3. --- Fingerprint Verification --- p.39Chapter 4.1.4. --- Individual Biometric Performance --- p.39Chapter 4.2. --- Verbal Information Verification (VIV) --- p.42Chapter 4.3. --- Chapter Summary --- p.44Chapter 5. --- Weighted Average Fusion for Multi-Modal Biometrics --- p.46Chapter 5.1. --- Experimental Setup and Results --- p.46Chapter 5.2. --- Analysis of Weighted Average Fusion Results --- p.48Chapter 5.3. --- Chapter Summary --- p.59Chapter 6. --- Fully Adaptive Fuzzy Logic Decision Fusion Framework --- p.61Chapter 6.1. --- Factors Considered in the Estimation of Biometric Sample Quality --- p.62Chapter 6.1.1. --- Factors for Speech --- p.63Chapter 6.1.2. --- Factors for Face --- p.65Chapter 6.1.3. --- Factors for Fingerprint --- p.70Chapter 6.2. --- Fuzzy Logic Decision Fusion Framework --- p.76Chapter 6.2.1. --- Speech Fuzzy Sets --- p.77Chapter 6.2.2. --- Face Fuzzy Sets --- p.79Chapter 6.2.3. --- Fingerprint Fuzzy Sets --- p.80Chapter 6.2.4. --- Output Fuzzy Sets --- p.81Chapter 6.2.5. --- Fuzzy Rules and Other Information --- p.83Chapter 6.3. --- Experimental Setup and Results --- p.84Chapter 6.4. --- Comparison Between Weighted Average and Fuzzy Logic Decision Fusion --- p.86Chapter 6.5. --- Chapter Summary --- p.95Chapter 7. --- Factors Affecting VIV Performance --- p.97Chapter 7.1. --- Factors from Verbal Messages --- p.99Chapter 7.1.1. --- Number of Distinct-Unique Responses --- p.99Chapter 7.1.2. --- Distribution of Distinct-Unique Responses --- p.101Chapter 7.1.3. --- Inter-person Lexical Choice Variations --- p.103Chapter 7.1.4. --- Intra-person Lexical Choice Variations --- p.106Chapter 7.2. --- Factors from Utterance Verification --- p.108Chapter 7.2.1. --- Thresholding --- p.109Chapter 7.2.2. --- Background Noise --- p.113Chapter 7.3. --- VIV Weight Estimation Using PDP --- p.115Chapter 7.4. --- Chapter Summary --- p.119Chapter 8. --- Adaptive Fusion for SV and VIV --- p.121Chapter 8.1. --- Weighted Average fusion of SV and VIV --- p.122Chapter 8.1.1. --- Scores Normalization --- p.123Chapter 8.1.2. --- Experimental Setup --- p.123Chapter 8.2. --- Adaptive Fusion for SV and VIV --- p.124Chapter 8.2.1. --- Components of Adaptive Fusion --- p.126Chapter 8.2.2. --- Three Categories Design --- p.129Chapter 8.2.3. --- Fusion Strategy for Each Category --- p.132Chapter 8.2.4. --- SV Driven Approach --- p.133Chapter 8.3. --- SV and Fixed-Pass Phrase VIV Fusion Results --- p.133Chapter 8.4. --- SV and Key-Pass Phrase VIV Fusion Results --- p.136Chapter 8.5. --- Chapter Summary --- p.141Chapter 9. --- Conclusions and Future Work --- p.143Chapter 9.1. --- Conclusions --- p.143Chapter 9.2. --- Future Work --- p.145Bibliography --- p.147Appendix A Detail of BSC Speech --- p.153Appendix B Fuzzy Rules for Multimodal Biometric Fusion --- p.155Appendix C Full Example for Multimodal Biometrics Fusion --- p.157Appendix DReason for Having a Flat Error Surface --- p.161Appendix E Reason for Having a Relative Peak Point in the Middle of the Error Surface --- p.164Appendix F Illustration on Fuzzy Logic Weight Estimation --- p.166Appendix GExamples for SV and Key-Pass Phrase VIV Fusion --- p.17

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Intelligent interface agents for biometric applications

This thesis investigates the benefits of applying the intelligent agent paradigm to biometric identity verification systems. Multimodal biometric systems, despite their additional complexity, hold the promise of providing a higher degree of accuracy and robustness. Multimodal biometric systems are examined in this work leading to the design and implementation of a novel distributed multi-modal identity verification system based on an intelligent agent framework. User interface design issues are also important in the domain of biometric systems and present an exceptional opportunity for employing adaptive interface agents. Through the use of such interface agents, system performance may be improved, leading to an increase in recognition rates over a non-adaptive system while producing a more robust and agreeable user experience. The investigation of such adaptive systems has been a focus of the work reported in this thesis. The research presented in this thesis is divided into two main parts. Firstly, the design, development and testing of a novel distributed multi-modal authentication system employing intelligent agents is presented. The second part details design and implementation of an adaptive interface layer based on interface agent technology and demonstrates its integration with a commercial fingerprint recognition system. The performance of these systems is then evaluated using databases of biometric samples gathered during the research. The results obtained from the experimental evaluation of the multi-modal system demonstrated a clear improvement in the accuracy of the system compared to a unimodal biometric approach. The adoption of the intelligent agent architecture at the interface level resulted in a system where false reject rates were reduced when compared to a system that did not employ an intelligent interface. The results obtained from both systems clearly express the benefits of combining an intelligent agent framework with a biometric system to provide a more robust and flexible application

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes