Introduction. Why Voices are Difficult to Discriminate Forensically. Forensic Phonetic Parameters. Expressing the Outcome. Characterizing Forensic Speaker Identification. The Human Vocal Tract and the Production and Description of Speech Sounds. Phonemics. Speech Acoustics. Speech Perception. What is a Voice? The Likelihood Ratio Revisited: A Demonstration of the Method. Summary and Envoi.

Forensic Speaker Identification

Earlier research has shown that established authors can be distinguished by measuring specific properties of their writings, their stylome as it were. Here, we examine writings of less experienced authors. We succeed in distinguishing between these authors with a very high probability, which implies that a stylome exists even in the general population. However, the number of traits needed for so successful a distinction is an order of magnitude larger than assumed so far. Furthermore, traits referring to syntactic patterns prove less distinctive than traits referring to vocabulary, but much more distinctive than expected on the basis of current generativist theories of language learning.

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New machine learning methods demonstrate the existence of a human stylome

https://openresearch-repository.anu.edu.au/bitstream/1885/54292/2/01_Morrison_Forensic_voice_comparison_and_2009.pdf

Forensic voice comparison and the paradigm shift

Applied Speech and Audio Processing is a MATLAB-based, one-stop resource that blends speech and hearing research in describing the key techniques of speech and audio processing This practically oriented text provides MATLAB examples throughout to illustrate the concepts discussed and to give the reader hands-on experience with important techniques Chapters on basic audio processing and the characteristics of speech and hearing lay the foundations of speech signal processing, which are built upon in subsequent sections explaining audio handling, coding, compression, and analysis techniques The final chapter explores a number of advanced topics that use these techniques, including psychoacoustic modelling, a subject which underpins MP3 and related audio formats With its hands-on nature and numerous MATLAB examples, this book is ideal for graduate students and practitioners working with speech or audio systems

/pdf/applied-speech-and-audio-processing-with-matlab-examples-58kse0axl1.pdf

Applied Speech and Audio Processing: With Matlab Examples

Non-contemporaneous speech samples from 27 male speakers of Australian English were compared in a forensic likelihood-ratio framework. Parametric curves (polynomials and discrete cosine transforms) were fitted to the formant trajectories of the diphthongs ∕aɪ∕, ∕eɪ∕, ∕oʊ∕, ∕aʊ∕, and ∕ɔɪ∕. The estimated coefficient values from the parametric curves were used as input to a generative multivariate-kernel-density formula for calculating likelihood ratios expressing the probability of obtaining the observed difference between two speech samples under the hypothesis that the samples were produced by the same speaker versus under the hypothesis that they were produced by different speakers. Cross-validated likelihood-ratio results from systems based on different parametric curves were calibrated and evaluated using the log-likelihood-ratio cost function (Cllr). The cross-validated likelihood ratios from the best-performing system for each vowel phoneme were fused using logistic regression. The resulting fused system had a very low error rate, thus meeting one of the requirements for admissibility in court.

Likelihood-ratio forensic voice comparison using parametric representations of the formant trajectories of diphthongs.

Although the development of state-of-the-art speaker recognition systems has shown considerable progress in the last decade, performance levels of these systems do not as yet seem to warrant large-scale introduction in anything other than relatively low-risk applications. Conditions typical of the forensic context such as differences in recording equipment and transmission channels, the presence of background noise and of variation due to differences in communicative context continue to pose a major challenge. Consequently, the impact of automatic speaker recognition technology on the forensic scene has been relatively modest and forensic speaker identification practice remains heavily dominated by the use of a wide variety of largely subjective procedures. While recent developments in the interpretation of the evidential value of forensic evidence clearly favour methods that make it possible for results to be expressed in terms of a likelihood ratio, unlike automatic procedures, traditional methods in the field of speaker identification do not generally meet this requirement. However, conclusions in the form of a binary yes/no-decision or a qualified statement of the probability of the hypothesis rather than the evidence are increasingly criticised for being logically flawed. Against this background, the need to put alternative validation procedures in place is becoming more widely accepted. Although speaker identification by earwitnesses differs in some important respects from the much more widely studied field of eyewitness identification, there are sufficient parallels between the two for speaker identification by earwitnesses to benefit greatly from a close study of the guidelines that have been proposed for the administration of line-ups in the visual domain. Some of the central notions are briefly discussed. Rapid technical developments in the world of telecommunications in which speech and data are increasingly transmitted through the same communication channels may soon blunt the efficacy of traditional telephone interception as an investigative and evidential tool. The gradual shift from analogue to digital recording media and the increasingly widespread availability of digital sound processing equipment as well as its ease of operation make certain types of manipulation of audio recordings comparatively easy to perform. If done competently, such manipulation may leave no traces and may therefore well be impossible to detect. Authorship attribution is another forensic area that has had a relatively chequered history. The rapid increase in the use of electronic writing media including e-mail, sms, and the use of ink jet printers at the expense of typewritten and to a lesser extent hand-written texts reduces the opportunities of authorship attribution by means of traditional document examination techniques and may create a greater demand for linguistic expertise in this area. A survey is provided of ongoing work in the area, based on reactions to a questionnaire sent out earlier this year.

A. P. A. Broeders

Papers

Forensic speech and audio analysis forensic linguistics