Assessing the admissibility of a new generation of forensic voice comparison testimony

This article provides a primer on forensic voice comparison (aka forensic speaker recognition), a branch of forensic science in which the forensic practitioner analyzes a voice recording in order to provide an expert opinion that will help the trier-of-fact determine the identity of the speaker. The article begins with an explanation of ways in which human speech varies within and between speakers. It then discusses different technical approaches that forensic practitioners have used to compare voice recordings, and frameworks of reasoning that practitioners have used for evaluating the evidence and reporting its strength. It then discusses procedures for empirical validation of the performance of forensic voice comparison systems. It also discusses the potential influence of contextual bias and ways to reduce this. Building on this scientific foundation, the article then offers analysis, commentary, and recommendations on how courts evaluate the admissibility of forensic voice comparison testimony under the Daubert and Frye standards. It reviews past rulings such as U.S. v. Angleton, 269 F.Supp 2nd 892 (S.D. Tex. 2003) that found expert testimony based on the spectrographic approach inadmissible under Daubert. The article also offers a detailed analysis of the evidence presented in the recent Daubert hearing in U.S. v. Ahmed, et al. 2015 EDNY 12-CR-661, which included testimony based on the newer automatic approach. The scientific testimony proffered in Ahmed is used to illustrate the issues courts are likely to face when considering the admissibility of forensic voice comparison testimony in the future. The article concludes with a discussion of how proponents of forensic voice comparison testimony might meet a reasonably rigorous application of the Daubert standard and thereby ensure that such testimony is sufficiently trustworthy to be used in court

Admissibility of forensic voice comparison testimony in England and Wales

In 2015 the Criminal Practice Directions (CPD) on admissibility of expert evidence in England and Wales were revised. They emphasised the principle that “the court must be satisfied that there is a sufficiently reliable scientific basis for the evidence to be admitted”. The present paper aims to assist courts in understanding from a scientific perspective what would be necessary to demonstrate the validity of testimony based on forensic voice comparison. We describe different technical approaches to forensic voice comparison that have been used in the UK, and critically review the case law on their admissibility. We conclude that courts have been inconsistent in their reasoning. In line with the CPD, we recommend that courts enquire as to whether forensic practitioners have made use of data and analytical methods that are appropriate and adequate for the case under consideration, and that courts require forensic practitioners to empirically demonstrate the level of performance of their forensic voice comparison system under conditions reflecting those of the case under consideration

An evidence-based forensic taxonomy of Windows phone communication apps

Communication apps can be an important source of evidence in a forensic investigation (e.g., in the investigation of a drug, trafficking or terrorism case where the communications apps were used by the accused persons during the transactions or planning activities)., This study presents the first evidence-based forensic taxonomy of Windows Phone communication apps, using an existing two-dimensional, Android forensic taxonomy as a baseline. Specifically, 30 Windows Phone communication apps, including Instant Messaging (IM) and Voice, over IP (VoIP) apps, are examined. Artifacts extracted using physical acquisition are analyzed, and seven digital evidence objects of forensic, interest are identified, namely: Call Log, Chats, Contacts, Locations, Installed Applications, SMSs and User Accounts. Findings from this study, would help to facilitate timely and effective forensic investigations involving Windows Phone communication apps

The Comparison of Audio Analysis Using Audio Forensic Technique and Mel Frequency Cepstral Coefficient Method (MFCC) as the Requirement of Digital Evidence

Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.Audio forensics is the application of science and scientific methods in handling evidence in the form of audio to support the disclosure of various criminal cases and to reveal information needed in the trial process. In this regard, a sound recording that has been made is then cut into words. Then, pieces of it were analyzed by using audio forensic techniques through parameters of pitch, formant and spectogram using Forensic Audio Technique Analysis on artificial voice recordings and live voice recordings. The analysis was also carried out using the extraction of the Mel Frequency Cepstral Coefficient (MFCC) feature, the Dynamic Time Warping (DTW) Method, and applying the K-Nearest Neighbor (KNN) algorithm. The testing results by using audio forensic techniques obtained an accuracy of 76.3%, meanwhile the accuracy of testing results by using a system that has been built (self-made system) is 66.7%

Forensic Evidence and the Court of Appeal for England and Wales

The Criminal Division of the Court of Appeal has extensively analyzed the role of forensic evidence. In doing so, the court has grappled with the admissibility and reliability of a broad range of forensic evidence, from DNA and computer forensics to medical and psychological proof, to more outlying subjects like facial mapping, fiber analysis, or voice identification. The court has analyzed these subjects from two perspectives: the admissibility of such evidence in the lower courts and the admissibility of such evidence as fresh evidence on appeal. In both contexts, the court has taken a practical approach to admitting forensic proof that is deemed to be helpful and reliable. It has also given helpful guidance to practitioners dealing with these issues, and to judges who must instruct juries about how to evaluate forensic evidence. Compared to the approach of the United States courts, the Court of Appeal has indicated a greater willingness to keep pace with scientific developments and to admit forensic proof that contributes to the accuracy of criminal verdicts

Information-theoretical comparison of evidence evaluation methods for score-based biometric systems

Ponencia presentada en la Seventh International Conference on Forensic Inference and Statistics, The University of Lausanne, Switzerland, August 2008Biometric systems are a powerful tool in many forensic disciplines in order to aid scientists to evaluate the weight of the evidence. However, uprising requirements of admissibility in forensic science demand scientific methods in order to test the accuracy of the forensic evidence evaluation process. In this work we analyze and compare several evidence analysis methods for score-based biometric systems. For all of them, the score given by the system is transformed into a likelihood ratio ( LR) which expresses the weight of the evidence. The accuracy of each LR computation method will be assessed by classical Tippett plots- We also propose measuring accuracy in terms of average information given by the evidence evaluation process, by means of Empirical Cross-Entropy (EC-E) plots. Preliminary results are presented using a voice biometric system and the NIST SRE 2006 experimental protocol

Voice Spectrography Evidence: Approaches to Admissibility

The admissibility of the results of voiceprint\u27 analysis as evidence in a criminal trial has received a great deal of attention in the last ten years, both from legal scholars and in the courts. Although a relative newcomer to the field of forensic science, voice spectrography is not a recent development in the field of evidence; Wigmore foresaw the use of a voiceprint as early as 1937, when he suggested that the individuality of a person\u27s voice provided a possible means of speaker identification

Forensic Carving of Wireless Network Information from the Android Linux Kernel

Modern smartphones integrate ubiquitous access to voice, data, and email communication and allow users to rapidly handle both personal and corporate business affairs. This is possible because of the smartphone’s constant connectivity with the Internet. Digital forensic investigators have long understood the value of smartphones as forensic evidence, and this thesis seeks to provide new tools to increase the amount of evidence that one can obtain and analyze from an Android smartphone. Specifically, by using proven data carving algorithms we try to uncover information about the phone’s connection to wireless access points in a capture of the device’s volatile memory

Forensic authorship classification by paragraph vectors of speech transcriptions

In forensic comparison, document classification techniques are used mainly for authorship classification and author profiling. In the present study, we aim to introduce paragraph vector modelling (by Doc2Vec) into the likelihoodratio framework paradigm of forensic evidence comparison. Transcriptions of spontaneous speech recording are used as input to paragraph vector extraction model training. Logistic regression models are trained based on cosine distances of paragraph vector pairs to predict the same and different author origin probability. Results are evaluated according to different speaking styles (transcriptions of speech tasks available in the dataset). Cllr and equal error rate values (lowest ones are 0.47 and 0.11, respectively) show that the method can be useful as a feature for forensic authorship comparison and may extend the voice comparison methods for speaker verification