Lexical coverage in ELF

The aim of this study was to determine how much vocabulary is needed to understand English in contexts where it is spoken internationally as a lingua franca (ELF). This information is critical to inform vocabulary size targets for second language (L2) learners of English. The current research consensus, based on native-English-speaker data, is that 6,000–7,000 word families plus proper nouns are needed. However, since English has become a global lingua franca, native speakers of English have become a minority: in fact, today, there are around two billion speakers of English worldwide, of which less than a quarter are native speakers. This means that non-native speakers of English are more likely to interact with other non-native speakers than with native speakers. Thus, using findings based on solely native-speaker data may not provide the most accurate information needed to inform vocabulary size targets for L2 learners of English. Indeed, this information needs to be supplemented with data from competent non-native speakers of English who can represent a legitimate model for L2 learners of English. This study uses the largest freely available corpus of general, spoken ELF in Europe: the one million-word Vienna-Oxford International Corpus of English (VOICE). The word family was used as a lexical counting unit, and the lexical coverage of VOICE was calculated for various thresholds of the most frequent word families in the corpus. A comparative analysis was carried out to determine the lexical coverage of VOICE provided by frequency ranked word lists based on data from the British National Corpus of English and the Contemporary Corpus of American English. The main findings of this study indicate that fewer than 3,000–4,000 word families plus proper nouns can provide the lexical resources needed to understand English in international contexts where it is spoken as a lingua franca. This is approximately half the number of word families (i.e. 6,000–7,000 word families plus proper nouns) which scholars have claimed are needed to understand spoken English. The findings of this study represent a substantial saving in vocabulary size targets for L2 learners of English who wish to be functional in understanding English spoken as an international lingua franca

Fact or Friction: examination of the transparency, reliability and sufficiency of the ACE-V method of fingerprint analysis

Three studies are presented which provide a mixed methods exploration of fingerprint analysis. Using a qualitative approach (Expt 1), expert analysts used a ‘think aloud’ task to describe their process of analysis. Thematic analysis indicated consistency of practice, and experts’ comments underpinned the development of a training tool for subsequent use. Following this, a quantitative approach (Expt 2) assessed expert reliability on a fingerprint matching task. The results suggested that performance was high and often at ceiling, regardless of the length of experience held by the expert. As a final test, the experts’ fingerprint analysis method was taught to a set of naïve students, and their performance on the fingerprint matching task was compared both to the expert group and to an untrained novice group (Expt 3). Results confirmed that the trained students performed significantly better than the untrained students. However, performance remained substantially below that of the experts. Several explanations are explored to account for the performance gap between experts and trained novices, and their implications are discussed in terms of the future of fingerprint evidence in court

SuperIdentity: fusion of identity across real and cyber domains

Under both benign and malign circumstances, people now manage a spectrum of identities across both real-world and cyber domains. Our belief, however, is that all these instances ultimately track back for an individual to reflect a single ‘SuperIdentity’. This paper outlines the assumptions underpinning the SuperIdentity Project, describing the innovative use of data fusion to incorporate novel real-world and cyber cues into a rich framework appropriate for modern identity. The proposed combinatorial model will support a robust identification or authentication decision, with confidence indexed both by the level of trust in data provenance, and the diagnosticity of the identity factors being used. Additionally, the exploration of correlations between factors may underpin the more intelligent use of identity information so that known information may be used to predict previously hidden information. With modern living supporting the ‘distribution of identity’ across real and cyber domains, and with criminal elements operating in increasingly sophisticated ways in the hinterland between the two, this approach is suggested as a way forwards, and is discussed in terms of its impact on privacy, security, and the detection of threa

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications

Forensic voice discrimination: the effect of speech type and background noise on performance

In forensic settings, lay (non‐expert) listeners may be required to compare voice samples for identity. In two experiments we investigated the effect of background noise and variations in speaking style on performance. In each trial, participants heard two recordings, responded whether the voices belonged to the same person, and provided a confidence rating. In Experiment 1, the first recording featured read speech, while the second featured read or spontaneous speech. Both recordings were presented in quiet, or with background noise. Accuracy was highest when recordings featured the same speaking style. In Experiment 2, background noise either occurred in the first or second recording. Accuracy was higher when it occurred in the second. The overall results reveal that both speaking style and background noise can disrupt accuracy. Whilst there is a relationship between confidence and accuracy in all conditions, it is variable. The forensic implications of these findings are discussed

Understanding person acquisition using an interactive activation and competition network

Face perception is one of the most developed visual skills that humans display, and recent work has attempted to examine the mechanisms involved in face perception through noting how neural networks achieve the same performance. The purpose of the present paper is to extend this approach to look not just at human face recognition, but also at human face acquisition. Experiment 1 presents empirical data to describe the acquisition over time of appropriate representations for newly encountered faces. These results are compared with those of Simulation 1, in which a modified IAC network capable of modelling the acquisition process is generated. Experiment 2 and Simulation 2 explore the mechanisms of learning further, and it is demonstrated that the acquisition of a set of associated new facts is easier than the acquisition of individual facts in isolation of one another. This is explained in terms of the advantage gained from additional inputs and mutual reinforcement of developing links within an interactive neural network system. <br/

Towards automated eyewitness descriptions: describing the face, body and clothing for recognition

A fusion approach to person recognition is presented here outlining the automated recognition of targets from human descriptions of face, body and clothing. Three novel results are highlighted. First, the present work stresses the value of comparative descriptions (he is taller than…) over categorical descriptions (he is tall). Second, it stresses the primacy of the face over body and clothing cues for recognition. Third, the present work unequivocally demonstrates the benefit gained through the combination of cues: recognition from face, body and clothing taken together far outstrips recognition from any of the cues in isolation. Moreover, recognition from body and clothing taken together nearly equals the recognition possible from the face alone. These results are discussed with reference to the intelligent fusion of information within police investigations. However, they also signal a potential new era in which automated descriptions could be provided without the need for human witnesses at all

Combining Forces: Data fusion across man and machine for biometric analysis

Through the HUMMINGBIRD framework outlined here,we seek to encourage a novel multidisciplinary approach to biometric analysis with the goal of enhancing both understanding and accuracy of identification