The use and utility of localised speech forms in determining identity: forensic and sociophonetic perspectives

该项目旨在调查英格兰东北部地区英语口音的变化。我们关注的是来自纽卡斯尔、桑德兰和米德尔斯堡的工人阶级的演讲。众所周知，这些口音彼此之间有很大的不同，但不太清楚是什么特定的发音导致听者将不同的发音归类为一种口音(通常是‘Geordie’)。该项目试图找出导致口音被归类在一起的特定特征，以便人们听到他们有别于英国北部的其他口音。同时，我们将确定听众使用什么声音将说话者归类为子组，例如纽卡斯尔对桑德兰。在这里，社会经济阶层的作用至关重要。根据这篇文献，允许听者将说话者分配到一个或另一个群体的本地化模式，传统上与来自较低社会经济群体的说话者有关，即“工人阶级”。然而，在这个项目中，我们问的是，经常出差或休闲的工人阶级演讲者的演讲是否与我们可能将其归类为从未工作/长期失业的经济边缘人的演讲存在明显差异。关于这一社会鸿沟的社会学研究已有很多，但尚未对其对人们话语的影响进行系统的研究。我们还将考虑性别和年龄差异，并评估数据是否有合理变化的迹象。研究表明，较大的流动性通过趋同言语习惯来促进语言的一致性，而有限的流动性则具有相反的效果。因此，我们预测，随着时间的推移，我们样本中更多的流动成员将在语言上汇聚在三个地方，而每个地方的经济边缘群体将变得更加多样化。该项目的第二条线索是关于个人层面的语音变化，而不是群体或社区层面的语音变化。这在法医领域尤其相关，因为在法医领域，个人身份至关重要。在刑事调查中，法医言语分析员执行两项主要任务。第一种是说话人侧写，即试图从一名身份不明的说话人的发言录音样本中确定其地理和社会来源，以协助警方识别潜在的嫌疑人。这项任务需要有关未知说话者可能所属社区的最新详细信息。鉴于其重点是确定高度本地化的言语形式，拟议项目产生的语料库将满足这些要求。第二项取证任务是说话人比对。在这里，专家比较两个语音样本，并评估它们由相同或不同说话者产生的可能性。越来越多地，这是以自动化的方式完成的，使用软件来提取关于录音的声学属性的信息。两个样本之间的相似程度是在具有相同或相似口音的说话者的录音的相关背景人群的背景下评估的。这在一定程度上衡量了样本的典型性。这种方法的问题是，理想情况下，人们应该为每个案例收集一个新的语料库，这可能会贵得令人望而却步。因此，如果能够识别语音信号中对口音变化相对不敏感的声学参数将是有利的。拟议的项目通过将东北地区的录音与现有语料库中明显不同的口音(标准的英国南方英语)的录音相结合，来测试这是否可能。如果这种方法被证明是合法的，它对法医语音分析社区的实用价值将是可以考虑的。因此，这一双线项目的发现将使相关的学术界受益，并在法医语音科学领域具有重要的应用价值。

The project aims to investigate variation in accents of English across Northeast England. We focus on the speech of working-class people from Newcastle, Sunderland and Middlesbrough. It is well established that these accents differ significantly from one another, but it is less clear what particular pronunciations lead listeners to group the varieties as one accent (typically, 'Geordie'). The project seeks to identify specific features that cause the accents to be classed together, such that they are heard to be distinct from other accents of northern Britain. Simultaneously, we will ascertain what sounds listeners use to classify speakers into subgroups, e.g. from Newcastle versus Sunderland. The role of socio-economic class is of key importance here. The localised patterns that allow listeners to assign speakers to one group or another are, according to the literature, traditionally associated with speakers from lower socio-economic groups, i.e. the 'working class'. However, in this project we ask whether clear differences exist between the speech of working-class speakers who routinely travel for work or leisure and that of economically marginal individuals we might label 'never worked/long-term unemployed'. Plentiful sociological research on this social divide exists, but how it impacts on people's speech has not yet been investigated systematically. We will also take gender and age differences into account, and assess the data for signs of sound change. It has been shown that greater mobility promotes linguistic uniformity through convergence of speech habits, while limited mobility has the opposite effect. We therefore predict that more mobile members of our sample would converge linguistically over time and across the three localities, while the economically marginal groups in each place would become more divergent.The project's second strand concerns speech variation at the level of the individual rather than the group or community. This is particularly relevant in the forensic domain, wherein individual identity is crucial. In criminal investigations, forensic speech analysts perform two main tasks. The first, speaker profiling, involves attempting to specify the geographical and social origins of an unknown speaker from a recorded sample of his/her speech, so as to assist the police in identifying potential suspects. The task requires detailed, up-to-date information about the speech of the community/ies to which the unknown speaker may belong. Given its focus on the identification of highly localised speech forms, the corpus produced by the proposed project will satisfy those requirements. The second forensic task is speaker comparison. Here, the expert compares two speech samples, and assesses the likelihood that they were produced by the same or different speakers. Increasingly, this is done in an automated way, using software that extracts information about the acoustic properties of the recordings. The level of similarity between the two samples is evaluated in the context of a relevant 'background population' of recordings of speakers with the same or similar accents. This yields a measure of the samples' typicality. The problem with this approach is that ideally one ought to collect a new corpus for every case, which is likely to be prohibitively expensive. It would be advantageous, therefore, if acoustic parameters in the speech signal which are relatively insensitive to accent variation could be identified. The proposed project tests whether this is possible by combining the Northeast recordings with those from an existing corpus of a markedly different accent (Standard Southern British English). If the approach proves legitimate, its practical value to the forensic speech analysis community would be considerable.Findings emerging from this two-stranded project will therefore benefit the relevant academic communities as well as having significant applied utility in the field of forensic speech science.