A voice is much more than just a string of words. Voices, unlike fingerprints, are inherently complex. They signal a great deal of information in addition to the intended message: the speakers' sex, for example, or their emotional state, or age. Although evidence from DNA analysis grabs the headlines, DNA can't talk. It can't be recorded planning,
The volume provides a multidimensional view of the complex science involved in determining whether a suspect’s voice truly matches forensic speech samples, collected by law enforcement and counter-terrorism agencies, that are associated ...
Author: Amy Neustein
Publisher: Springer Science & Business Media
Category: Technology & Engineering
Forensic Speaker Recognition: Law Enforcement and Counter-Terrorism is an anthology of the research findings of 35 speaker recognition experts from around the world. The volume provides a multidimensional view of the complex science involved in determining whether a suspect’s voice truly matches forensic speech samples, collected by law enforcement and counter-terrorism agencies, that are associated with the commission of a terrorist act or other crimes. While addressing such topics as the challenges of forensic case work, handling speech signal degradation, analyzing features of speaker recognition to optimize voice verification system performance, and designing voice applications that meet the practical needs of law enforcement and counter-terrorism agencies, this material all sounds a common theme: how the rigors of forensic utility are demanding new levels of excellence in all aspects of speaker recognition. The contributors are among the most eminent scientists in speech engineering and signal processing; and their work represents such diverse countries as Switzerland, Sweden, Italy, France, Japan, India and the United States. Forensic Speaker Recognition is a useful book for forensic speech scientists, speech signal processing experts, speech system developers, criminal prosecutors and counter-terrorism intelligence officers and agents.
A reference for professionals who work with recorded evidence, covering areas such authentication of speech recordings, voice stress analysis, and speaker identification.
Author: Harry Francis Hollien
Publisher: Academic Press
Category: Automatische Spracherkennung
A reference for professionals who work with recorded evidence, covering areas such authentication of speech recordings, voice stress analysis, and speaker identification. It covers the basic sciences behind voice identification. It also covers what the reader needs to know about coordinating 'ear witness' lineups.
This mismatch has to be considered in the estimation of the likelihood ratio because it can introduce important errors. In this work, we handle and analyze this state-of-the-art system.
Author: Víctor Alonso Moreno
English: Nowadays, under controlled recording conditions, the state-of-the-art automatic speaker recognition systems show very good performance in discriminating between voices of speakers. However, in investigative activities (e.g., anonymous calls and wire-tapping) the conditions in which recordings are made cannot be controlled and pose a challenge to automatic speaker recognition. Some factors that introduce variability in the recordings can be the differences in the phone handset, in the transmission channel and in the recording devices. The strength of evidence, estimated using statistical models of within-source variability and between-sources variability, is expressed as a likelihood ratio, i.e., the probability of observing the features of the questioned recording in the statistical model of the suspected speaker's voice given the two competing hypotheses: the suspected speaker is the source of the questioned recording and the speaker at the origin of the questioned recording is not the suspected speaker. The main unresolved problem in forensic automatic speaker recognition today is that of handling mismatch in recording conditions. This mismatch has to be considered in the estimation of the likelihood ratio because it can introduce important errors. In this work, we handle and analyze this state-of-the-art system. The forensic automatic speaker recognition system consists of many parts, such as feature extraction and modeling. We have focused on the modeling part, training models which can be decomposed in two spaces, the speaker and session subspace. This technique, called Joint Factor Analysis, is the state-of-the-art in the speaker verification systems. Using the property of decomposition in two subspaces, we try to solve the problem of mismatched conditions adapting the session subspace of the train recordings to a new session subspace (which is under different conditions). To estimate the speaker and session subspaces, we need some databases, e.g. one database containing the traces, and another containing recordings from the suspect. These databases must be recorded in several conditions to simulate a real forensic case where mismatched is present. Examples to such recording conditions are cellular phones or fixed telephone network. Finally, an evaluation of the system is presented at the end of the work. Thanks to this evaluation, we see which recording conditions degrade more the results, what effect the mismatch have on the results and, how much the adaptation can fix these effects.
Almut Braun carried out forensic phonetic speaker identification experiments (voice lineups) with 306 lay listeners.
Author: Almut Braun
Almut Braun carried out forensic phonetic speaker identification experiments (voice lineups) with 306 lay listeners. Blind listeners significantly outperformed sighted listeners when the speech recordings were presented in studio quality. For recordings in mobile phone quality or of whispering voices, blind and sighted listeners achieved similar results. The data can be used as reference material for real cases with blind earwitnesses. Furthermore, it is discussed whether blind individuals are particularly suitable to work as forensic audio analysts for law enforcement agencies.
The methodology developed in this chapter has been adapted by the same authors to “Methodological Guidelines for Best Practice in Forensic Semiautomatic and Automatic Speaker Recognition” elaborated in the framework of ENFSI FSAAWG ...
Author: Massimo Tistarelli
This comprehensive handbook addresses the sophisticated forensic threats and challenges that have arisen in the modern digital age, and reviews the new computing solutions that have been proposed to tackle them. These include identity-related scenarios which cannot be solved with traditional approaches, such as attacks on security systems and the identification of abnormal/dangerous behaviors from remote cameras. Features: provides an in-depth analysis of the state of the art, together with a broad review of the available technologies and their potential applications; discusses potential future developments in the adoption of advanced technologies for the automated or semi-automated analysis of forensic traces; presents a particular focus on the acquisition and processing of data from real-world forensic cases; offers an holistic perspective, integrating work from different research institutions and combining viewpoints from both biometric technologies and forensic science.
Speaker Recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices.
Author: Viplav Gautam
Publisher: LAP Lambert Academic Publishing
Speaker Recognition is the computing task of validating a user's claimed identity using characteristics extracted from their voices. This technique is one of the most useful and popular biometric recognition techniques in the world especially related to areas in which security is a major concern. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities.It can be divided into Speaker Identification and Speaker Verification. Speaker identification determines which registered speaker provides a given utterance from amongst a set of known speakers. Speaker verification accepts or rejects the identity claim of a speaker - is the speaker the person they say they are?
Author: Zeno J. M. H. GeradtsPublish On: 2009-08-05
Speaker recognition is the general term used to include all of the many different tasks of discriminating people based on the sound of their voices. Forensic speaker recognition involves the comparison of recordings of an unknown voice ...
Author: Zeno J. M. H. Geradts
This book constitutes the refereed proceedings of the Third International Workshop, IWCF 2009, held in The Hague, The Netherlands, August 13-14, 2009. The 16 revised full papers presented were carefully reviewed and are organized in topical sections on speech and linguistics, fingerprints, handwriting, documents, printers, multimedia and visualization. This volume is interesting to researchers and professionals who deal with forensic problems using computational methods. Its primary goal is the discovery and advancement of forensic knowledge involving modeling, computer simulation, and computer-based analysis and recognition in studying and solving forensic problems.
1.2 Forensic Speaker Recognition Speaker recognition is the general term used to include all of the many different tasks of discriminating people based on the sound of their voices. In particular, forensic speaker recognition (FSR) is ...
Author: Virginio Cantoni
This book constitutes the proceedings of the First International Workshop on Biometric Authentication, BIOMET 2014, which was held in Sofia, Bulgaria, in June 2014. The 16 full papers presented in this volume were carefully reviewed and selected from 21 submissions. Additionally, this volume also contains 5 invited papers. The papers cover a range of topics in the field gait and behaviour analysis; iris analysis; speech recognition; 3D ear recognition; face and facial attributes analysis; handwriting and signature recognition; and multimodal and soft biometrics.