What does method validation look like for forensic voice comparison by a human expert?

Method validation has gained traction within forensic speech science. The community recognises the need to demonstrate that the analysis methods used are valid, but finding a way to do so has been more straightforward for some analysis methods than for others. This article addresses the issue of method validation for the Auditory Phonetic and Acoustic (AuPhA) approach to forensic voice comparison. Although it is possible to take inspiration from general regulatory guidance on method validation, it is clear that these cannot be transposed on to all forensic analysis methods with the same degree of success. Particularly with respect to an analysis method like AuPhA, and in a field of the size and characteristics of forensic speech science, a bespoke approach to method validation is required. In this article we address the discussions that have been taking place around method validation, and illustrate one possible solution to demonstrating the validity of voice comparison by a human expert using the AuPhA method. In doing so we consider the constraints placed on sole practitioners, which generally go unacknowledged.

Proposing Problem-Based Learning for teaching future forensic speech scientists.

In the last 10-15 years, Masters programmes and undergraduate modules have emerged in the UK that teach forensic speech science. Forensic speech science is the forensic subdiscipline concerned with analysing speech recordings, such as telephone calls of unknown speakers, when they arise as evidence. In order to answer questions surrounding the identity of the speakers in these recordings, forensic speech analysts draw on their expertise in phonetics and acoustics. Even though existing UK forensic speech science programmes do not claim to train students to a level where they are in a position to carry out real-life forensic casework, a proportion of the graduates from these programmes do go on to fill discipline-specific roles in security organisations or for private providers of forensic speech analysis. It is therefore surely in the community's interests to review educational approaches to capitalise on the current training opportunities. This paper specifically proposes to explore the potential of a Problem-Based Learning (PBL) approach to forensic speech science teaching. PBL is a student-centred learning approach that heavily relies on the students' independence in the solving of ill-structured problems. PBL has shown to be beneficial to programmes that directly lead on to discipline-specific professional roles, and has even become the standardised teaching approach in some of those areas (medicine being the flagship example). Given its reported success in other disciplines, the question arises as to whether PBL could bring similar benefits to prospective forensic speech practitioners and to forensic speech science as a whole.

Evolutionary remodelling of N-terminal domain loops fine-tunes SARS-CoV-2 spike.

The emergence of SARS-CoV-2 variants has exacerbated the COVID-19 global health crisis. Thus far, all variants carry mutations in the spike glycoprotein, which is a critical determinant of viral transmission being responsible for attachment, receptor engagement and membrane fusion, and an important target of immunity. Variants frequently bear truncations of flexible loops in the N-terminal domain (NTD) of spike; the functional importance of these modifications has remained poorly characterised. We demonstrate that NTD deletions are important for efficient entry by the Alpha and Omicron variants and that this correlates with spike stability. Phylogenetic analysis reveals extensive NTD loop length polymorphisms across the sarbecoviruses, setting an evolutionary precedent for loop remodelling. Guided by these analyses, we demonstrate that variations in NTD loop length, alone, are sufficient to modulate virus entry. We propose that variations in NTD loop length act to fine-tune spike; this may provide a mechanism for SARS-CoV-2 to navigate a complex selection landscape encompassing optimisation of essential functionality, immune-driven antigenic variation and ongoing adaptation to a new host.

Mechanisms of feature binding in visual working memory are stable over long delays.

The ability to accurately retain the binding between the features of different objects is a critical element of visual working memory. The underlying mechanism can be elucidated by analyzing correlations of response errors in dual-report experiments, in which participants have to report two features of a single item from a previously viewed stimulus array. Results from separate previous studies using different cueing conditions have indicated that location takes a privileged role in mediating binding between other features, in that largely independent response errors have been observed when location was used as a cue, but errors were highly correlated when location was one of the reported features. Earlier results from change detection tasks likewise support such a special role of location, but they also suggest that this role is substantially reduced for longer retention intervals in favor of object-based representation. In the present study, we replicated the findings of previous dual-report tasks with different cueing conditions, using matched stimuli and procedures. Moreover, we show that the observed patterns of error correlations remain qualitatively unchanged with longer retention intervals. Fits with neural population models demonstrate that the behavioral results at long, as well as short, delays are best explained by memory representations in independent feature maps, in which an item's features are bound to each other only via their shared location.

Voicing concerns: The balance between data protection principles and research developments in forensic speech science.

The status of forensic speech recordings among existing data protection guidance is not clear. The inherent nature of voice and the way in which forensic speech casework is currently allocated mean that there are additional barriers to incorporating real casework data into research activities. The key objective of this work is to explore data protection solutions that could enable the forensic speech science community to responsibly use real casework data for research and development purposes. While reviewing relevant guidance and rulings, issues such as proportionality, opportunism and data minimisation are addressed, as well as where voice sits in relation to the definition of "biometric data". This paper ultimately places forensic speech recordings in the data protection context to illuminate the specific issues that arise for this data type.

General Northern English. Exploring Regional Variation in the North of England With Machine Learning.

In this paper, we present a novel computational approach to the analysis of accent variation. The case study is dialect leveling in the North of England, manifested as reduction of accent variation across the North and emergence of General Northern English (GNE), a pan-regional standard accent associated with middle-class speakers. We investigated this instance of dialect leveling using random forest classification, with audio data from a crowd-sourced corpus of 105 urban, mostly highly-educated speakers from five northern UK cities: Leeds, Liverpool, Manchester, Newcastle upon Tyne, and Sheffield. We trained random forest models to identify individual northern cities from a sample of other northern accents, based on first two formant measurements of full vowel systems. We tested the models using unseen data. We relied on undersampling, bagging (bootstrap aggregation) and leave-one-out cross-validation to address some challenges associated with the data set, such as unbalanced data and relatively small sample size. The accuracy of classification provides us with a measure of relative similarity between different pairs of cities, while calculating conditional feature importance allows us to identify which input features (which vowels and which formants) have the largest influence in the prediction. We do find a considerable degree of leveling, especially between Manchester, Leeds and Sheffield, although some differences persist. The features that contribute to these differences most systematically are typically not the ones discussed in previous dialect descriptions. We propose that the most systematic regional features are also not salient, and as such, they serve as sociolinguistic regional indicators. We supplement the random forest results with a more traditional variationist description of by-city vowel systems, and we use both sources of evidence to inform a description of the vowels of General Northern English.

Automatic sociophonetics: Exploring corpora with a forensic accent recognition system.

This paper demonstrates how the Y-ACCDIST system, the York ACCDIST-based automatic accent recognition system [Brown (2015). Proceedings of the International Congress of Phonetic Sciences, Glasgow, UK], can be used to inspect sociophonetic corpora as a preliminary "screening" tool. Although Y-ACCDIST's intended application is to assist with forensic casework, the system can also be exploited in sociophonetic research to begin unpacking variation. Using a subset of the PEBL (Panjabi-English in Bradford and Leicester) corpus, the outputs of Y-ACCDIST are explored, which, it is argued, efficiently and objectively assess speaker similarities across different linguistic varieties. The ways these outputs corroborate with a phonetic analysis of the data are also discovered. First, Y-ACCDIST is used to classify speakers from the corpus based on language background and region. A Y-ACCDIST cluster analysis is then implemented, which groups speakers in ways consistent with more localised networks, providing a means of identifying potential communities of practice. Additionally, the results of a Y-ACCDIST feature selection task that indicates which specific phonemes are most valuable in distinguishing between speaker groups are presented. How Y-ACCDIST outputs can be used to reinforce more traditional sociophonetic analyses and support qualitative interpretations of the data is demonstrated.