Investigating the Phonetic Properties of Word Tokens in Interactional Contexts and the Implications for Forensic Voice Comparison

法医说话人比对(FSC)使用语音分析来协助刑事案件，通常涉及将罪犯(例如辱骂的语音邮件)的语音样本与已知嫌疑人的样本进行比较，以评估这两个样本由同一说话人制作的可能性。FSC依赖于在个人语音中变化较小但在说话者之间变化很大的特征。FSC经常使用音段特征(辅音和元音)。会话分析(CA)领域发现人们如何相互交谈的模式。CA使用基于话轮、功能和动作的量化数据。这两个领域都分析语音，但很少共享方法论。语篇和互动信息对FSC的潜在帮助还鲜有人研究。所进行的分析仅限于频率计数，例如不流畅。这项研究将更进一步，使用声学数据的跨学科方法。话语标记语是本研究的理想互动特征。DMS是频繁出现的、几乎没有命题意义的“空洞表达”，经常被污蔑、简短、多功能，并出现在许多位置(Brinton 2010)。例如，我的意思是，“我的意思是，事实上，我很累了”。FSC通常涉及较短的语音样本，例如电话呼叫或录音窃听，因此频繁的特征如DM是有用的。尽管DM的频率很高，但从未被分析用于说话人识别的目的。然而，有相当多的证据表明，它们应该提供有用的特定于说话人的信息。对Like的语音变异的研究表明，Like的语音变异是基于话语功能和周围语境的系统模式。Drager(2007)和Schleef&Turton(2016)分析了Like作为动词、副词、引语(例如“She‘s Like’no way‘)、DM和连词之间的区别。他们比较了与话语位置的边界“强度”相关的声学测量。这些研究得出结论：动词标记在元音中的声学运动比引号标记大，但比Dms小。因此，LIKE的引语发音更接近云雀，而不是DM标记。较强的边界包围‘like’也会导致较少的元音移动和减少/k/。对填充停顿(FP；即，呃，嗯)的声学分析也产生了特定于说话人的模式(Hughes等人。本文将在我的硕士学位论文的基础上，使用CA方法将对Like和FP的分析扩展到各种话语标记语和其他词语。分析将涉及到每个符号的功能、话轮位置和话轮功能。采取更全面、跨学科的方法将揭示更多关于DM和说话者使用DM的方式，同时也会告知FSC案例的方法。这些考虑导致了一系列研究问题：RQ 1：DM的发音有哪些方面的差异？问2：说话者对DM的发音是否一致，并以特定于说话者的方式发音，从而使它们具有有用的取证功能？问3：改变语境(即功能和转弯位置)如何影响说话者发音DM的方式？问4：这些背景是否增加了DM的取证潜力？为了回答问题1-3，我将在白玫瑰大学举办的一系列语料库中研究话语标记语的语音变异。分析将涉及定量语音方法，包括统计模型，如最近的通用加法混合模型，我在我的硕士论文中成功地将其应用于‘Like’和‘yes’。本文的研究将是一次创新性的尝试，将CA和FSS结合起来，拓宽取证方法和话语标记语的社会语音研究的范围。它将直接告知如何使用以前未被探索的演讲方面来帮助法医案件工作，从而在演讲是一种证据的情况下，增加司法问题的清晰度。

Forensic speaker comparison (FSC) uses phonetic analysis to aid criminal cases and typically involves comparing a speech sample made by a criminal (e.g. abusive voicemails) with a sample from a known suspect to assess the likelihood of the two samples being made by the same speaker. FSC relies on features which have low variation within an individual's speech but are highly variable between speakers. FSC often uses segmental features (consonants & vowels). The field of Conversation Analysis (CA) finds patterns in how people talk to each other. CA uses quantitative data based on turns, function and action.These fields both analyse speech, but they rarely share methodologies. The potential of discourse and interactional information to aid FSC has been little researched. What analysis has been conducted is limited to frequency counts, for example of disfluencies. This study will go further, using interdisciplinary methods on acoustic data. Discourse markers (DMs) are ideal interactional features for this research. DMs are "empty expressions" which appear frequently, have little propositional meaning and are often stigmatized, short, multifunctional and seen in many positions (Brinton 2010). Examples include actually and I mean as in "I mean, actually, I'm very tired". FSC often involves short speech samples, e.g. phone calls or recording bugs, therefore frequent features such as DMs are useful.Despite their frequency, DMs have never been analysed for the purposes of speaker identification. However, there is considerable evidence that they should provide useful speaker-specific information. Research into the phonetic variation of like shows systematic patterning based on utterance function and surrounding context. Drager (2007) and Schleef & Turton (2016) analysed the differences between like as a verb, adverb, quotative (e.g. "she was like 'no way'"), DM and conjunction. They compared acoustic measures related to boundary 'strength' of the discourse position. These studies concluded that verb tokens have greater acoustic movement within the vowel than quotative tokens, but less than DMs. Thus, quotative occurrences of like sound closer to 'lark' than DM tokens. A stronger boundary surrounding 'like' also leads to less vowel movement and a reduction of /k/. Acoustic analysis of filled pauses (FPs; i.e. uh, um) has also yielded speaker-specific patterns (Hughes et al. 2016).This thesis will extend the analysis of 'like' and FPs to a variety of DMs and other words using CA methods; building on my MSc dissertation. Analysis will refer to each token's function, turn position and the turn function. Taking a more holistic, interdisciplinary approach will unveil more about DMs and the way speakers use them whilst also informing the methods of FSC casework.These considerations lead to a series of research questions: RQ 1: In what ways do the pronunciations of DMs vary? RQ 2: Do speakers pronounce DMs consistently, and in a speaker-specific way, thereby making them useful forensic features? RQ 3: How does changing the context (i.e. function and turn position) impact the way speakers pronounce DMs? RQ 4: Do these contexts increase the forensic potential of DMs? To answer RQs 1-3, I will study the phonetic variation of DMs in a range of speech corpora held at White Rose universities. The analysis will involve quantitative phonetic methods, including statistical models such as the recent General Additive Mixed Models, which I successfully applied to 'like and 'yeah' in my MSc dissertation. I will also gather qualitative insights from CA which will inform how each word token will be interpreted.This research will be an innovative attempt at uniting CA and FSS to broaden the scope of forensic methods and the sociophonetic study of DMs. It will directly inform how a previously unexplored aspect of speech can be used to aid forensic casework, thereby adding clarity to issues of justice wherever speech is a piece of evidence.