Forensic Speaker Identification (International Forensic Science and Investigation)

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Contents

  1. Every Crime Leaves Traces….
  2. Updates & News for Members
  3. Biometrics in Forensic Identification: Applications and Challenges
  4. Forensic linguistics
  5. Forensic Science Disciplines - ANZPAA Website

Proceedings of Interspeech, Portland.

Every Crime Leaves Traces….

Voice disguise and automatic speaker recognition. Forensic Science International , 2—3 , — This unresolved case involves several members of his family and is very famous in France. We will get back to these aspects later, in the light of the Bayesian decision framework. Let us imagine that we have a perfect system which outputs perfect LRs. Now, something like a constant background noise disturbs this system and adds a constant bias to its output. Of course, the C LLR of the system will improve significantly while its discrimination power is still the same. This choice could be questioned by several other variants like average difficulty selection, random selection or auditory-based selection.

Merrell Dow Pharmaceuticals, Inc. Performance and reliability in voice biometrics. Would you like to be regularly informed by e-mail about our new publications in your fields of interest? Subscribe to our newsletter. Peter Lang on Facebook. This site uses cookies in order to improve your experience with this site. By clicking accept, you are agreeing to our use of cookies. User Account Sign In Not registered?

Updates & News for Members

Show Less Open access. Inter-individual variation in speech is a topic of increasing interest both in human sciences and speech technology. It can yield important insights into biological, cognitive, communicative, and social aspects of language. Written by specialists in psycholinguistics, phonetics, speech development, speech perception and speech technology, this volume presents experimental and modeling studies that provide the reader with a deep understanding of interspeaker variability and its role in speech processing, speech development, and interspeaker interactions. It discusses how theoretical models take into account individual behavior, explains why interspeaker variability enriches speech communication, and summarizes the limitations of the use of speaker information in forensics.

Linguistics Download PDF 4. Biometric technology makes a contribution to crime detection by associating the traces to the persons stored in the database, ranking the identity of persons and selecting subdivision of persons from which the trace may originate.

A biometric system is a pattern recognition device that acquires physical or behavioral data from an individual, extracts a salient feature set from the data, compares this feature set against the features set stored in the database and provides the result of the comparison. Therefore, a biometric system is composed of four modules [ 2 ]:.


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This component acquires the raw biometric data of an individual by scanning and reading. HASR is an interesting and specific case as it merges phonetic-forensic aspects with the aspects of automatic approaches. These unique feature sets are then matched against a stored fingerprint database. Revisiting the perspective opened by Doddington et al.

Furthermore, it also helps the expert to follow a scientific approach, since work is based only on evidence E.

Biometrics in Forensic Identification: Applications and Challenges

The distribution of scores generated from pairs of samples from different persons is called an impostor distribution, and the score distribution generated from pairs of samples of the same person is called a genuine distribution [ 16 ] Figure 1. In Proceedings of Interspeech, Antwerp , — A in phonetics from the Australian National University in which she conducted research on dialectal variation in Chinese.

For example, In case of fingerprint recognition , an optical fingerprint sensor may be used to image the ridge pattern of the fingertip. The quality of raw data is influenced by the scanning or camera device that is used. Quality assessment and feature extraction module: For further processing, the quality of the acquired raw data is first assessed. The raw data is subjected to signal enhancement algorithm to improve its quality.

This data is then processed and a set of salient features extracted to represent the underlying trait.

Forensic linguistics

This feature set is stored in the database and is referred as template. For example, the position and orientation of minutia in a fingerprint image is extracted by the feature extraction module in finger print biometric system. Matching and decision making module: In this module, the extracted templates are then matched against the stored templates and a matching score is given. On the basis of the matching score, the identity of a person is validated or ranked.

This module acts as storage of biometric system. During the enrolment process, the template extracted from raw biometric data is stored in the database along with some biographic information such as name, address, etc. The selection of each biometric trait depends on the variety of issues besides its matching criteria.

Every individual who is using the biometric application must possess the trait.


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The trait must show a sufficient difference across individuals comprising the population. The given biometric trait should not change significantly over a period of time. The trait should be easy to get and digitize and should not cause inconvenience to the individual. It should also be amenable to process further in order to extract features from the acquired data.

Forensic Speaker Identification International Forensic Science and Investigation

The recognition accuracy and the resources acquired to achieve that accuracy must meet the constraints imposed by the individual. Individuals that will access the biometric device should be willing to present their biometric traits to the system. It refers to the ease with which the trait of a person can be imitated or copied by using artefacts e.

The biometric system should be immune to the circumvention. Biometrics system can be classified into two main categories on the basis of application mode: In this process, the system conducts a one to many comparison to prove the identity of a person. In the verification mode, the biometrics information of an individual, who claims certain identity, is compared with his own biometric template stored in the system database.

This is also referred as one to one comparison. Biometric technologies find a place in crime detection in a number of ways: The implementation of automated fingerprint identification system AFIS in established the first application of biometrics where the automation of identity verification was based on the ten print cards. As a consequence of the development of mobile telecommunication and camera surveillance technologies CCTV , speaker, face and gait recognition became important biometric tools in the After the interest rose for soft biometric modalities such as body measurements height, width, weight and proportions, gender, hair, skin colour and clothing characteristics.

This interest was mainly motivated by the possibility of capturing these features in unconstraint environments [ 3 ]. In the forensic context, a test sample obtained from a crime scene is referred as crime scene sample, traces material and questioned item whereas the reference sample that is compared against the crime scene sample is named controlled material or known item.

Some of the trace samples biological traces, finger marks, earmarks, bite marks and lip marks are collected physically while others are acquired digitally face, voice, body measurements and gait. The particular biometric trait needs to be unique, distinctive and robust to the forensic conditions. Therefore, finger-marks and biological traces are searched in priority on a crime scene. The performance of a biometric system is largely influence by the quality of input sample conditioned by the acquisition and environmental conditions of a crime scene.

It is one of the most important tools of crime detection because of their robustness and uniqueness. A fingerprint is the pattern of friction ridges and valleys on the surface of a fingertip. In order to match a print, a fingerprint technician digitalizes or scans the print obtained at a crime scene and computer algorithms of a biometric system locate all the unique minutia and ridge points of a questioned print. IAFIS provides automated fingerprint search capabilities, latent searching capability, electronic image storage, and electronic exchange of fingerprints and responses.

IAFIS houses the fingerprints and criminal histories of 70 million subjects in the criminal master file, 31 million civil prints and fingerprints from 73, known and suspected terrorists processed by the U. The average response time for an electronic criminal fingerprint submission is about 27 min, while electronic civil submissions are processed within an hour and 12 min. The Ministry of Home Affairs, Government of India is also going to set up a national fingerprint database of 28 lakh convicts to enable speedy identification of offenders and expedite ongoing probes.

Biometric face recognition technology plays an important role in law enforcement. This feature set is stored in the database and is referred as template. For example, the position and orientation of minutia in a fingerprint image is extracted by the feature extraction module in finger print biometric system.

Matching and decision making module: In this module, the extracted templates are then matched against the stored templates and a matching score is given. On the basis of the matching score, the identity of a person is validated or ranked. System database module: This module acts as storage of biometric system. During the enrolment process, the template extracted from raw biometric data is stored in the database along with some biographic information such as name, address, etc.

The selection of each biometric trait depends on the variety of issues besides its matching criteria. Jain et al. Universality: Every individual who is using the biometric application must possess the trait. Uniqueness: The trait must show a sufficient difference across individuals comprising the population. Permanence: The given biometric trait should not change significantly over a period of time.

Forensic Science Disciplines - ANZPAA Website

Measurability: The trait should be easy to get and digitize and should not cause inconvenience to the individual. It should also be amenable to process further in order to extract features from the acquired data. Performance: The recognition accuracy and the resources acquired to achieve that accuracy must meet the constraints imposed by the individual. Acceptability: Individuals that will access the biometric device should be willing to present their biometric traits to the system. Circumvention: It refers to the ease with which the trait of a person can be imitated or copied by using artefacts e.

The biometric system should be immune to the circumvention. Biometrics system can be classified into two main categories on the basis of application mode: Verification and Identification. In this process, the system conducts a one to many comparison to prove the identity of a person. In the verification mode, the biometrics information of an individual, who claims certain identity, is compared with his own biometric template stored in the system database.

This is also referred as one to one comparison. Biometric technologies find a place in crime detection in a number of ways: a The modules and techniques of biometrics help in analyzing the evidence by overcoming the limitations of human cognitive abilities and thus increases efficiency and effectiveness of investigation b These methods provide scientific basis by applying techniques of computer science, applied mathematics and statistics and standardization for crime investigation procedure by analyzing huge bulk of data which are not humanly possible c These techniques provide the advantages of visualizing and documenting the result of analysis.

The implementation of automated fingerprint identification system AFIS in established the first application of biometrics where the automation of identity verification was based on the ten print cards. As a consequence of the development of mobile telecommunication and camera surveillance technologies CCTV , speaker, face and gait recognition became important biometric tools in the After the interest rose for soft biometric modalities such as body measurements height, width, weight and proportions, gender, hair, skin colour and clothing characteristics.

This interest was mainly motivated by the possibility of capturing these features in unconstraint environments [ 3 ]. In the forensic context, a test sample obtained from a crime scene is referred as crime scene sample, traces material and questioned item whereas the reference sample that is compared against the crime scene sample is named controlled material or known item. Some of the trace samples biological traces, finger marks, earmarks, bite marks and lip marks are collected physically while others are acquired digitally face, voice, body measurements and gait.