From swallowing to speech to singing: investigating the vocal tract using electromagnetic articulography and ultrasound
2018-19 (University of York Priming Fund: £68,186.96)
with George Bailey, Amelia Gully, Eleanor Chodroff, Helena Daffern, Nick Pears (University of York)
This project seeks to purchase equipment for analysis of speech articulation that will put York at the cutting edge of voice science research, bringing together researchers from different disciplines across the University who all have the same aim: to understand the complexities of the human voice and vocal tract. The result will be a world-class laboratory producing novel interdisciplinary research with applications in technology, healthcare and security, and establishing national and international networks for researchers in voice science at York.
WikiDialects: Creating an Online Resource for Accent Descriptions
2019-20 (IAFPA: £1,300)
with Jessica Wormald (J P French and University of York)
This project will create an open-access, Wikipedia-style repository for descriptive information about the linguistic and phonetic patterns within language varieties (focussing initially on British English). We call it WikiDialects. The repository will contain summaries of literature from phonetics and sociolinguistics organised by regional and social groups, and by linguistic variable. It will also signpost users to sources of more detailed information and provide links to relevant academic papers. The primary aim of WikiDialects is to be a central, ‘go-to’ resource for forensic phoneticians when assessing the typicality of a given variable within the relevant population in casework which uses auditory and/or acoustic analysis. WikiDialects will also be a valuable resource for other disciplines that require baseline descriptions of language varieties, such as speech and language therapy, sociolinguistics, and language teaching and learning.
Voice and Identity: Source, Filter, Biometric
2015-18 (AHRC: £892,000)
with Paul Foulkes, Peter French, Philip Harrison (University of York)
The aim of the project is to compare the performance of different methods for forensic voice (or speaker) comparison – from linguistics and phonetics, acoustics, and automatic speaker recognition (ASR) – on the same set of recordings. We will explore the performance of the methods to assess their relative strengths, the consistency of their results and error patterns, and thus the potential for different methods to be integrated into a single framework. The ultimate aim is to improve methods in forensic voice comparison, taking a major step towards the development of a methodology that is more transparent, validated, and replicable. This outcome will benefit academics and forensic practitioners, the public, judicial systems, and investigative/security agencies.
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Modelling Features for Forensic Speaker Comparison
2013-15 (BA/Leverhulme: £9,848)
with Paul Foulkes, Peter French, Philip Harrison (University of York), Erica Gold (University of Huddersfield), Colin Aitken (University of Edinburgh), Tereza Neocleous (University of Glasgow)
In forensic speaker comparison (FSC), experts compare speech patterns in criminal and suspect audio recordings to assess the evidence under competing prosecution and defence hypotheses, i.e. the criminal voice is that of the suspect versus that of someone else. There is a move toward expressing expert evidence in the form of Bayesian likelihood ratios. Speech presents considerable difficulties for this approach, as different types of data are analysed in forensic casework: linguistic data can be normally or non-normally distributed; variables can be continuous or discrete; and complex correlations exist between variables. It is imperative to develop statistical models that cater for these difficulties.
This project brings together leading forensic statisticians with forensic phoneticians. We will explore typical forensic phonetic data to assess the value of complex datasets for statistical modelling. We aim to develop new statistical models that incorporate a broader array of phonetic variables into FSC analyses and thus quantify forensic phonetic evidence more reliably.
Identifying Correlations Between Speech Parameters for Forensic Speaker Comparisons
2013-14 (IAFPA: £1,400)
with Erica Gold (University of Huddersfield)
Building on a pilot study carried out by the applicants (Gold and Hughes 2012), the project sets out to investigate two aspects of correlation between speech parameters. The first involves empirical testing of data from a homogeneous group of speakers (DyViS: Nolan 2009) to reveal correlations that may exist between traditional acoustic-phonetic parameters commonly used in forensic speaker comparisons. Secondly, we aim to address theoretical issues underlying the application of logistic regression fusion (Brümmer et al. 2007) in a likelihood ratio (LR) framework, by comparing the levels of correlations found in the data against the levels of correlations found for LRs computed by a given system. The results have two sets of implications. Firstly, the results will provide an empirically-based starting point for making informed decisions concerned with the combination of parameters in real forensic speaker comparisons. This applies both to experts working in a LR framework who must account for naïve Bayes, as well as those working in other frameworks where the expert personally selects parameters to consider and combine in casework. Those making uninformed assumptions in regards to correlations that may exist between parameters used in casework could potentially carry out analyses which lead to miscarriages of justice in the representation of the strength of evidence. Secondly, the results are relevant in addressing how appropriate fusion is as a method for combining dependent parameters using LRs. For those working within a LR framework, the results are intended to provide a basis for the development of a Bayesian network (Taroni et al. 2006) to create a ‘front-end’ mathematical model of interdependencies between speech parameters in order to appropriately combine parameters.