Traci Walker

Managing Problems of Acceptability Through High Rise-Fall Repetitions

This article examines one of the ways in which matters of truth, appropriateness, and acceptability are raised and managed within the course of everyday conversation. Using the methodology of conversation analysis, we show that by repeating what another participant has said and doing so with a high rise-fall intonation contour, a speaker claims that the repeated talk is “wrong” and in need of correction. There is an incongruity between two versions of the world—the one presented in the repeated speakers talk and the one the repeating speaker knows or believes to be true, appropriate, or acceptable. The ensuing sequences are routinely expanded and morally charged as the participants jostle for epistemic or moral authority over the matter at hand and work to repair the incongruity (even if, in the end, they agree to disagree).

Form ≠ Function: The independence of prosody and action

This article argues for the importance of describing form independently of function, especially for prosodic and phonetic forms. Form and function are often conflated by language-in-interaction researchers when they give descriptive labels to the sound of talk (e.g., “upgraded” pitch, “continuing” intonation), and that tempts researchers to see a given form as having a given function or practice—often one that is influenced by the descriptive label. I argue that we should discipline ourselves to keeping to a purely technical description of any form (practice); that will then make it possible unambiguously to show how that form contributes to a particular function (action), without presuming the relationship to be exclusive. Data are in American and British English.

Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints

BACKGROUND The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentiate between cognitive difficulties due to neurodegenerative disorders (ND) or functional memory disorders (FMD). OBJECTIVE This study explores whether the differential diagnostic analysis of doctor-patient interactions in a memory clinic can be automated. METHODS Verbatim transcripts of conversations between neurologists and patients initially presenting with memory problems to a specialist clinic were produced manually (15 with FMD, and 15 with ND). A range of automatically detectable features focusing on acoustic, lexical, semantic, and visual information contained in the transcripts were defined aiming to replicate the diagnostic qualitative observations. The features were used to train a set of five machine learning classifiers to distinguish between ND and FMD. RESULTS The mean rate of correct classification between ND and FMD was 93% ranging from 97% by the Perceptron classifier to 90% by the Random Forest classifier.Using only the ten best features, the mean correct classification score increased to 95%. CONCLUSION This pilot study provides proof-of-principle that a machine learning approach to analyzing transcripts of interactions between neurologists and patients describing memory problems can distinguish people with neurodegenerative dementia from people with FMD.

Diagnosing People with Dementia Using Automatic Conversation Analysis.

A recent study using Conversation Analysis (CA) has demonstrated that communication problems may be picked up during conversations between patients and neurologists, and that this can be used to differentiate between patients with (progressive neurodegenerative dementia) ND and those with (nonprogressive) functional memory disorders (FMD). This paper presents a novel automatic method for transcribing such conversations and extracting CA-style features. A range of acoustic, syntactic, semantic and visual features were automatically extracted and used to train a set of classifiers. In a proof-of-principle style study, using data recording during real neurologist-patient consultations, we demonstrate that automatically extracting CA-style features gives a classification accuracy of 95%when using verbatim transcripts. Replacing those transcripts with automatic speech recognition transcripts, we obtain a classification accuracy of 79% which improves to 90% when feature selection is applied. This is a first and encouraging step towards replacing inaccurate, potentially stressful cognitive tests with a test based on monitoring conversation capabilities that could be conducted in e.g. the privacy of the patient’s own home.

The Independence of Phonetic Form and Interactional Accomplishments

In this response to Peter Auers commentary, I revisit the question of phonetic form and interactional meaning as well as the question of what the aim of transcription actually is (or should be). What I advocate is a careful look at the ways in which our analyses link linguistic forms with actions.

Dementia detection using automatic analysis of conversations

Abstract Neurogenerative disorders, like dementia, can affect a person’s speech, language and as a consequence, conversational interaction capabilities. A recent study, aimed at improving dementia detection accuracy, investigated the use of conversation analysis (CA) of interviews between patients and neurologists as a means to differentiate between patients with progressive neurodegenerative memory disorder (ND) and those with (non-progressive) functional memory disorders (FMD). However, doing manual CA is expensive and difficult to scale up for routine clinical use. In this paper, we present an automatic classification system using an intelligent virtual agent (IVA). In particular, using two parallel corpora of respectively neurologist- and IVA-led interactions, we show that using acoustic, lexical and CA-inspired features enable ND/FMD classification rates of 90.0% for the neurologist-patient conversations, and 90.9% for the IVA-patient conversations. Analysis of the differentiating potential of individual features show that some differences exist between the IVA and human-led conversations, for example in average turn length of patients.

Developing an intelligent virtual agent to stratify people with cognitive complaints: A comparison of human–patient and intelligent virtual agent–patient interaction:

Previous work on interactions in the memory clinic has shown that conversation analysis can be used to differentiate neurodegenerative dementia from functional memory disorder. Based on this work, a screening system was developed that uses a computerised ‘talking head’ (intelligent virtual agent) and a combination of automatic speech recognition and conversation analysis-informed programming. This system can reliably differentiate patients with functional memory disorder from those with neurodegenerative dementia by analysing the way they respond to questions from either a human doctor or the intelligent virtual agent. However, much of this computerised analysis has relied on simplistic, nonlinguistic phonetic features such as the length of pauses between talk by the two parties. To gain confidence in automation of the stratification procedure, this paper investigates whether the patients’ responses to questions asked by the intelligent virtual agent are qualitatively similar to those given in response to a doctor. All the participants in this study have a clear functional memory disorder or neurodegenerative dementia diagnosis. Analyses of patients’ responses to the intelligent virtual agent showed similar, diagnostically relevant sequential features to those found in responses to doctors’ questions. However, since the intelligent virtual agent’s questions are invariant, its use results in more consistent responses across people – regardless of diagnosis – which facilitates automatic speech recognition and makes it easier for a machine to learn patterns. Our analysis also shows why doctors do not always ask the same question in the exact same way to different patients. This sensitivity and adaptation to nuances of conversation may be interactionally helpful; for instance, altering a question may make it easier for patients to understand. While we demonstrate that some of what is said in such interactions is bound to be constructed collaboratively between doctor and patient, doctors could consider ensuring that certain, particularly important and/or relevant questions are asked in as invariant a form as possible to be better able to identify diagnostically relevant differences in patients’ responses.

Phonetic and Sequential Differences of Other-Repetitions in Repair Initiation

ABSTRACT This article analyzes two different repair initiation practices that both utilize other-repetition. We call these framing and prefacing other-repetitions and show that they are treated as making different claims about the speakers’ depth of understanding of the prior talk. Framing repetitions repeat the turn-initial components of the prior turn with a particular “long and flat” phonetic pattern; prefacing repetitions consist of a minimal repetition of the final grammatical structures of the prior speaker’s talk, produced quietly and with a falling intonation contour. While framing repetitions are treated as displays of either a hearing or simple understanding problem, prefacing repetitions claim a more serious breakdown of understanding. Data are in British and American English.

PO029 An avatar aid in memory clinic

Objects and Aims Referrals to secondary care memory clinic has more than tripled. This has led to increased demand on diagnostic services. Conversation Analysis (CA) can help in the screening for dementia. In CA important features of the conversation between doctor, patient are analysed. In this project we created an avatar to automatically initiate conversation. Methods The avatar, a computerised head asked questions created by consulting neurologists to which patients responded verbally. Audio and video data was collected and a simple interface with two buttons (‘start/forward’ and ‘repeat’) on a keyboard was used. Audio recordings were transcribed and annotated by the CA expert. Results 24 participants were recruited. All finished the trial without problems. We received feedback from participants, many saying they felt the avatar was easy to talk to. The CA expert correctly classified in 6 of 7 cases of Functional Memory disorder (FMD) and 4 out of 6 cases of neurodegenerative dementia. Conclusion An Avatar has potential to be a low cost addition to memory screening

Displays and claims of understanding in conversation by people with aphasia

Background: There is scope for additional research into the specific linguistic and sequential structures used in speech and language therapist (SLT)-led therapeutic conversations with people with aphasia (PWA). Whilst there is some evidence that SLTs use different conversational strategies than the partners of PWA, research to date has focussed mainly on measuring the effects of conversation-based therapies—not on analysing therapeutic conversations taking place between SLTs and PWA. Aims: This paper presents an analysis of the use of oh-prefacing by some PWA during therapeutic supported conversations with SLTs. Methods & Procedures: Normally occurring therapeutic conversations between SLTs and PWA after stroke were qualitatively analysed using Conversation Analysis. Interactions with five PWA were video-recorded, involving three different specialist stroke SLTs. Outcomes & Results: The analysis revealed a difference in the way some PWA use turns that display understanding (e.g., oh right) versus those that continue the conversation, merely claiming understanding (e.g., right). This use of oh-prefacing is similar to that described in the literature on typical conversations. In our data, SLTs are shown to treat oh-prefaced turns differently from non-oh-prefaced turns, by pursuing the topic in the latter, and progressing on to a new topic in the former. Conclusions: At least some PWA use oh-prefacing in the same way as non-language-impaired adults to display understanding of information versus merely claiming to understand. The SLTs in our data are shown to treat non-oh-prefaced turns as mere claims of understanding by providing the PWA with additional information, using supported conversation techniques, and pursuing additional same-topic talk, whereas oh-prefaced turns are treated as displays of understanding by being confirmed, and leading to changes of topic. This study is a first step in providing SLTs with a clearer understanding of the ways in which they are assessing the understanding of PWA, which may in turn help them better support non-therapy staff.