Using BIG data to understand the BIG picture: Overcoming heterogeneity in speech for forensic applications

Forensic speech science (FSS) - an applied sub-discipline of phonetics - has come to play a critical role in criminal cases involving voice evidence. Within FSS, Forensic speaker comparison (FSC) involves the comparison of a criminal recording (e.g. a threatening phone call), and a known suspect sample (e.g. a police interview). It is the role of an expert forensic phonetician to advise the trier of fact (e.g. judge or jury) on the likelihood of the two samples coming from the same speaker. There are two important elements involved in making such a comparison. First, the expert will carry out an assessment of the similarity of the speech characteristics in the criminal recording and the suspect sample. Second, the expert will assess the degree to which the same speech features for the criminal sample can be considered to be typical for a given speaker group. The speaker group will typically be defined by age, sex and geographical region (or accent). This second element is critical in providing context for the first; the suspect could have speech very similar to that in the criminal recording but this could be purely coincidental if they exhibit speech characteristics that are common to their speaker group. In contrast, if the criminal and suspect are observed as having speech features considered as being atypical for their speaker group then this would provide strong evidence for it being the same speaker.
One complication associated with FSC is that data to estimate whether a speech feature is typical or atypical for the given speaker group, commonly known as population data, are scarcely available. Population data are typically obtained by collecting a set of recordings containing the voices of a homogeneous group of speakers similar in age, sex, and geographical region (or accent). Unfortunately, the time and expense involved in the collection of population data means that forensic phoneticians face a huge challenge in obtaining such data for casework. This problem is further complicated by the high degree of variation that exists in speech across different speaker groups. Methodological research in the field of FSS has demonstrated that identifying the correct population for a FSC is vital in accurately representing the strength of evidence. It is largely for these reasons that experts argue that the biggest problem facing the field is the limited availability of population data.
The primary aim of this research is to explore a novel set of proposed methods that seek to remedy the aforementioned problems. The current lack of a platform on which to exchange data means that population data for a specific speaker group might have already been collected, unbeknown to experts in need of such data. This project intends to bring an end to this type of scenario by developing an international platform on which to share data, and also encouraging fellow researchers and experts to participate in data sharing. In addition, the project will explore the extent to which population data are generalizable; specifically, this will entail identifying the geographical (or regional accent) level at which speaker groups can be defined. For example, an expert might define a population group as having a Leeds accent, when in actuality a population defined more generally as West Yorkshire would suffice. This would clearly have implications for the way in which population data would be collected.
In order to explore the issue of defining the population data, a West Yorkshire (WY) database of 200 male speakers will be collected (including 50 speakers from each of the four urban areas: Huddersfield, Leeds, Bradford, and Wakefield). The database will be used to test the sensitivity of the strength of evidence when FSC cases are simulated using varying definitions of accent for the population data. In addition to serving methodological purpose, the WY database will also serve as a practical resource for casework and research in its own right.