Voice-signature-based Speaker Recognition

Magister Scientiae - MSc (Computer Science)Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these have thus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information. This they did mostly by means of locks, passwords, smartcards and biometrics. Verifying individuals by using their physical or behavioural features is more secure than using other data such as passwords or smartcards, because everyone has unique features which distinguish him or her from others. Furthermore the biometrics of a person are difficult to imitate or steal. Biometric technologies represent a significant component of a comprehensive digital identity solution and play an important role in security. The technologies that support identification and authentication of individuals is based on either their physiological or their behavioural characteristics. Live-­‐data, in this instance the human voice, is the topic of this research. The aim is to recognize a person’s voice and to identify the user by verifying that his/her voice is the same as a record of his / her voice-­‐signature in a systems database. To address the main research question: “What is the best way to identify a person by his / her voice signature?”, design science research, was employed. This methodology is used to develop an artefact for solving a problem. Initially a pilot study was conducted using visual representation of voice signatures, to check if it is possible to identify speakers without using feature extraction or matching methods. Subsequently, experiments were conducted with 6300 data sets derived from Texas Instruments and the Massachusetts Institute of Technology audio database. Two methods of feature extraction and classification were considered—mel frequency cepstrum coefficient and linear prediction cepstral coefficient feature extraction—and for classification, the Support Vector Machines method was used. The three methods were compared in terms of their effectiveness and it was found that the system using the mel frequency cepstrum coefficient, for feature extraction, gave the marginally better results for speaker recognition

Voice signature based Speaker Recognition

Magister Scientiae - MSc (Computer Science)Personal identification and the protection of data are important issues because of the ubiquitousness of computing and these havethus become interesting areas of research in the field of computer science. Previously people have used a variety of ways to identify an individual and protect themselves, their property and their information

Robust speaker recognition using both vocal source and vocal tract features estimated from noisy input utterances.

Wang, Ning.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 106-115).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction to Speech and Speaker Recognition --- p.1Chapter 1.2 --- Difficulties and Challenges of Speaker Authentication --- p.6Chapter 1.3 --- Objectives and Thesis Outline --- p.7Chapter 2 --- Speaker Recognition System --- p.10Chapter 2.1 --- Baseline Speaker Recognition System Overview --- p.10Chapter 2.1.1 --- Feature Extraction --- p.12Chapter 2.1.2 --- Pattern Generation and Classification --- p.24Chapter 2.2 --- Performance Evaluation Metric for Different Speaker Recognition Tasks --- p.30Chapter 2.3 --- Robustness of Speaker Recognition System --- p.30Chapter 2.3.1 --- Speech Corpus: CU2C --- p.30Chapter 2.3.2 --- Noise Database: NOISEX-92 --- p.34Chapter 2.3.3 --- Mismatched Training and Testing Conditions --- p.35Chapter 2.4 --- Summary --- p.37Chapter 3 --- Speaker Recognition System using both Vocal Tract and Vocal Source Features --- p.38Chapter 3.1 --- Speech Production Mechanism --- p.39Chapter 3.1.1 --- Speech Production: An Overview --- p.39Chapter 3.1.2 --- Acoustic Properties of Human Speech --- p.40Chapter 3.2 --- Source-filter Model and Linear Predictive Analysis --- p.44Chapter 3.2.1 --- Source-filter Speech Model --- p.44Chapter 3.2.2 --- Linear Predictive Analysis for Speech Signal --- p.46Chapter 3.3 --- Vocal Tract Features --- p.51Chapter 3.4 --- Vocal Source Features --- p.52Chapter 3.4.1 --- Source Related Features: An Overview --- p.52Chapter 3.4.2 --- Source Related Features: Technical Viewpoints --- p.54Chapter 3.5 --- Effects of Noises on Speech Properties --- p.55Chapter 3.6 --- Summary --- p.61Chapter 4 --- Estimation of Robust Acoustic Features for Speaker Discrimination --- p.62Chapter 4.1 --- Robust Speech Techniques --- p.63Chapter 4.1.1 --- Noise Resilience --- p.64Chapter 4.1.2 --- Speech Enhancement --- p.64Chapter 4.2 --- Spectral Subtractive-Type Preprocessing --- p.65Chapter 4.2.1 --- Noise Estimation --- p.66Chapter 4.2.2 --- Spectral Subtraction Algorithm --- p.66Chapter 4.3 --- LP Analysis of Noisy Speech --- p.67Chapter 4.3.1 --- LP Inverse Filtering: Whitening Process --- p.68Chapter 4.3.2 --- Magnitude Response of All-pole Filter in Noisy Condition --- p.70Chapter 4.3.3 --- Noise Spectral Reshaping --- p.72Chapter 4.4 --- Distinctive Vocal Tract and Vocal Source Feature Extraction . . --- p.73Chapter 4.4.1 --- Vocal Tract Feature Extraction --- p.73Chapter 4.4.2 --- Source Feature Generation Procedure --- p.75Chapter 4.4.3 --- Subband-specific Parameterization Method --- p.79Chapter 4.5 --- Summary --- p.87Chapter 5 --- Speaker Recognition Tasks & Performance Evaluation --- p.88Chapter 5.1 --- Speaker Recognition Experimental Setup --- p.89Chapter 5.1.1 --- Task Description --- p.89Chapter 5.1.2 --- Baseline Experiments --- p.90Chapter 5.1.3 --- Identification and Verification Results --- p.91Chapter 5.2 --- Speaker Recognition using Source-tract Features --- p.92Chapter 5.2.1 --- Source Feature Selection --- p.92Chapter 5.2.2 --- Source-tract Feature Fusion --- p.94Chapter 5.2.3 --- Identification and Verification Results --- p.95Chapter 5.3 --- Performance Analysis --- p.98Chapter 6 --- Conclusion --- p.102Chapter 6.1 --- Discussion and Conclusion --- p.102Chapter 6.2 --- Suggestion of Future Work --- p.10

The Rise of iWar: Identity, Information, and the Individualization of Modern Warfare

During a decade of global counterterrorism operations and two extended counterinsurgency campaigns, the United States was confronted with a new kind of adversary. Without uniforms, flags, and formations, the task of identifying and targeting these combatants represented an unprecedented operational challenge for which Cold War era doctrinal methods were largely unsuited. This monograph examines the doctrinal, technical, and bureaucratic innovations that evolved in response to these new operational challenges. It discusses the transition from a conventionally focused, Cold War-era targeting process to one optimized for combating networks and conducting identity-based targeting. It analyzes the policy decisions and strategic choices that were the catalysts of this change and concludes with an in depth examination of emerging technologies that are likely to shape how this mode of warfare will be waged in the future.https://press.armywarcollege.edu/monographs/1436/thumbnail.jp

Robust speaker recognition in presence of non-trivial environmental noise (toward greater biometric security)

The aim of this thesis is to investigate speaker recognition in the presence of environmental noise, and to develop a robust speaker recognition method. Recently, Speaker Recognition has been the object of considerable research due to its wide use in various areas. Despite major developments in this field, there are still many limitations and challenges. Environmental noises and their variations are high up in the list of challenges since it impossible to provide a noise free environment. A novel approach is proposed to address the issue of performance degradation in environmental noise. This approach is based on the estimation of signal-to-noise ratio (SNR) and detection of ambient noise from the recognition signal to re-train the reference model for the claimed speaker and to generate a new adapted noisy model to decrease the noise mismatch with recognition utterances. This approach is termed “Training on the fly” for robustness of speaker recognition under noisy environments. To detect the noise in the recognition signal two different techniques are proposed: the first technique including generating an emulated noise depending on estimated power spectrum of the original noise using 1/3 octave band filter bank and white noise signal. This emulated noise become close enough to original one that includes in the input signal (recognition signal). The second technique deals with extracting the noise from the input signal using one of speech enhancement algorithm with spectral subtraction to find the noise in the signal. Training on the fly approach (using both techniques) has been examined using two feature approaches and two different kinds of artificial clean and noisy speech databases collected in different environments. Furthermore, the speech samples were text independent. The training on the fly approach is a significant improvement in performance when compared with the performance of conventional speaker recognition (based on clean reference models). Moreover, the training on the fly based on noise extraction showed the best results for all types of noisy data

Sources of the communicative body

This study provides evidence for the warranted assertion that classroom practices will be enhanced by awareness of how non-linguistic modalities of the face, hands and vocal intonation contribute to cohesive and cooperative strategies within social groups. Both the history and observations of non-linguistic communication presented by this study suggest that visual, kinesic, and spatial comprehension create and influence social fields and common spaces, yet our language for these fields and spaces is impoverished. This knowledge has been submerged and marginalized through history. At the same time, through time, despite this submersion and marginalization, interrelational and intrarelational synchrony and dis-synchrony, centered on and by the communicative body, occur in social settings in ways that can be considered from both historical and observational perspectives. Buildi ng on recent theory by Damasio, Donald, Noddings, Grumet, Terdiman, and Nussbaum, the historical concepts and classroom observations presented here evidence that social values such as caring, loyalty, and generosity are sometimes understood, implicitly and explicitly, through the exchange, perception, and interpretation of non-linguistic signs. By understanding how the face and hands and rhythm and pitch of the voice create cohesive and cooperative social values in learning spaces - separate from racial, ethnic, and intellectual differences - this investigation recovers a submerged knowledge in order to offer a new logic for understanding social process. In turn, this new logic hopes to further transformational practice in the learning and teaching arts and sciences