Investigating the Phonetic Properties of Word Tokens in Interactional Contexts and the Implications for Forensic Voice Comparison
Lead Research Organisation:
University of York
Department Name: Language and Linguistic Science
Abstract
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.
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.
