Forensic Speaker Identity Verification (F-SIV) in Italy: First Evaluation Campaign Evalita-2009 (original) (raw)
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We report here the results of a first timid attempt to promote an evaluation campaign on a Forensic Speaker Identity Verification task within Evalita 2009. Participants were prompted to test methods and models usually used in forensics on a common corpus collected simulating real forensic characteristics and situations. The Task presented a Training data set including known suspected voices to be compared with voices in two other data sets, namely a Closed-test set of 16 unknown voices and an Open-test set containing different voices to be segmented before the test or comparison. Results achieved by participants are here briefly reported.
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This paper discusses the application of automatic speaker verification systems in forensic casework. A framework for reporting the system outcome is proposed. Specific system requirements to properly cope with forensic idiosyncrasies are analyzed through a series of simulations. Results suggest that the design of a forensic speaker verification system not necessarily match the settings of current state-of-the-art systems.
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To clarify some major points in current Forensic Speaker Identification practice using a list of "weaknesses in the science" as perceived by the legal profession (Hayne & Crockett, 1995: 2,3). MODELS OF INFORMATION CONTENT IN SPEECH Simple model: transmission of information in "Speech Chain": idea-> production (how acoustic disturbances are produced by speaker)-> acoustics (physical structure of transmitted speech wave)-> perception (how listener decodes acoustic structure to understand its information content.) Fact-Acoustic output of a speaker is uniquely determined by the speaker's anatomy. Fact-it is relatively easy, even for medium quality recordings, to extract and quantify acoustic parameters from recorded speech, and to use these to characterise the speaker as they are speaking on that particular occasion.
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Important aspects of Technical Forensic Speaker Recognition, particularly those associated with evidence, are exemplified and critically discussed, and comparisons drawn with generic Speaker Recognition. The centrality of the Likelihood Ratio of BayesÕ theorem in correctly evaluating strength of forensic speech evidence is emphasised, as well as the many problems involved in its accurate estimation. It is pointed out that many different types of evidence are of use, both experimentally and forensically, in discriminating same-speaker from different-speaker speech samples, and some examples are given from real forensic case-work to illustrate the Likelihood Ratio-based approach. The extent to which Technical Forensic Speaker Recognition meets the Daubert requirement of testability is also discussed.
Tools for forensic speaker recognition
In press in: F. Orletti, L. Mariottini (Eds.), Forensic Communication. Theories, practice and instruments, Cambridge Scholar Publishing, Cambridge., 2017
In this work, we present and discuss a new software application—IMPAVIDO (Integrated Methods for PArametric Voice IDentificatiOn)— which aims to provide a development environment to test different techniques for FSR. Accordingly, both IDEM and SMART methodologies have been re-implemented (emulated, as best as our knowledge) in order to work together in the same environment. Here, we assess their performances on some operating conditions, focusing on accuracy metric estimation such as the Cllr and Tippet plots.
CIVIL Corpus: Voice Quality for Speaker Forensic Comparison
Procedia - Social and Behavioral Sciences, 95 (587-593), 2013
The most frequent way in which criminals disguise their voices implies changes in phonation types, but it is difficult to maintain them for a long time. This mechanism severely hampers identification. Currently, the CIVIL corpus comprises 60 Spanish speakers. Each subject performs three tasks: spontaneous conversation, carrier sentences and reading, using modal, falsetto and creak(y) phonation. Two different recording sessions, one month apart, were conducted for each speaker, who was recorded with microphone, telephone and electroglottography. This is the first (open-access) corpus of disguised voices in Spanish. Its main purpose is finding biometric traces that remain in voice despite disguise.
Results of the 2003 NFI-TNO Forensic Speaker Recognition Evaluation
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NOTION: Journal of Linguistics, Literature, and Culture
Technological developments, especially in AI applications, allow someone to commit criminal acts by changing other people's voices. This study implements forensic linguistics by comparing data sources on the audio of Lesti (L) and Rizky Billar (RB) to ensure the originality of the sounds. The Speaker Profiling method with aural-perception analysis is used because there is no valid comparative evidence, so the audio comparison is taken from the L and RB platforms published to the public. This research resulted in two different aspects of each audio: phonetics and acoustics. These two aspects show the two audio sources' differences in pronunciation, emphasis, frequency, and tone. As a result, the vote is identified as a hoax. This research can demonstrate the use of software to help identify individual language profiles; it contributes to the development of applied linguistics, especially as a legal aid tool. Researchers hope that there will be further research from other ling...