Chapter 40 Prosodic Impairments Bill Wells, Bill WellsSearch for more papers by this authorTraci Walker, Traci WalkerSearch for more papers by this author Bill Wells, Bill WellsSearch for more papers by this authorTraci Walker, Traci WalkerSearch for more papers by this author Book Editor(s):Martin J. Ball, Martin J. BallSearch for more papers by this authorNicole Müller, Nicole MüllerSearch for more papers by this authorElizabeth Spencer, Elizabeth SpencerSearch for more papers by this author First published: 08 January 2024 https://doi.org/10.1002/9781119875949.ch40 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Abstract Every time we speak, we have to do something with the pitch, loudness and duration of the utterance. Linguists sometimes refer to these features as "suprasegmental," suggesting that they are somehow above a string of consonants and vowels. This connotation is misleading: rather, in speech the string of consonants and vowels is overlaid onto a base of phonation (voicing), generated by an airstream from the lungs passing through the larynx, which results in fluctuations in pitch (height and movement), and loudness distributed over phonatory chunks of varying durations. The term "prosody" and the related adjective prosodic, are commonly used to refer to features of pitch, loudness, and duration in speech, in a broad sense – encompassing their use on individual words (e.g. in lexical stress; duration of the syllable or part of syllable; lexical tones), as well as the use of these features over longer stretches of speech (phrases, complete utterances, conversational turns), the latter being the focus of the present chapter. There are at least two good reasons why clinical linguists and speech and language pathology professionals should study prosody. First, there are some clients who present with unusual prosodic patterns, and it is important to investigate why this might be. Second, if prosody is a relative strength for many people with speech and language difficulties, how might it be used to support or compensate for other aspects of language? In the case of prosody, the basis for postulating an impairment is likely to be the auditory impression of listeners that the speaker's use of prosodic features is in some way atypical for that speech community, yet its atypicality cannot be attributed to other causes, for example, being a non‐native speaker whose prosody in the second language is influenced by the mother tongue. Beyond that, identification, description, and explanation of the impairment are theory dependent. The investigator can adopt one or more relatively distinct though complementary approaches. References Aichert , I. , Spaeth , M. , & Ziegler , W. ( 2016 ). The role of metrical information in apraxia of speech. 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Everyone should have the opportunity to participate in decisions about their health, including people living with dementia. People with dementia typically bring a companion to medical appointments, so most care decisions are made in interactions involving three parties. To make decisions about their care, patients with dementia must have the opportunity to take a turn-at-talk in conversations where decisions are made. However, negotiating who speaks next in triadic talk is a complex task, especially when dementia-associated language and/or memory problems impact communication. Findings show that using second person ("you") pronouns assist people with dementia in responding to queries, yet third person ("she/he") can exclude them from the interaction, although this near-canonical pronoun use can be overridden by sequential placement, gesture, and gaze. We also demonstrate how midturn pronoun switching often only provides for tokenistic inclusion, though this again is dependent on sequential placement and embodied interaction. Data are in English.

Objective A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce currently high misdiagnosis rates and waiting times for specialist assessments. Most clinical decision tools based on patient-reported symptom inventories only distinguish between two of the three most common causes of TLOC (epilepsy, functional /dissociative seizures, and syncope) or struggle with the particularly challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic seizures and FDS seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions. Method Participants completed an online web application that consisted of a 34-item medical history and symptom questionnaire (iPEP) and spoken interaction with a virtual agent (VA) that asked eight questions about the most recent experience of TLOC. Support Vector Machines (SVM) were trained using different combinations of features and nested leave-one-out cross validation. The iPEP provided a baseline performance. Inspired by previous qualitative research three spoken language based feature sets were designed to assess: 1) formulation effort, 2) the proportion of words from different semantic categories, and 3) verb, adverb, and adjective usage. Results 76 participants completed the application (Epilepsy = 24, FDS = 36, syncope = 16). Only 61 participants also completed the VA interaction (Epilepsy = 20, FDS = 29, syncope = 12). The iPEP model accurately predicted 65.8% of all diagnoses, but the inclusion of the language features increased the accuracy to 85.5% by improving the differential diagnosis between epilepsy and FDS. Conclusion These findings suggest that an automated analysis of TLOC descriptions collected using an online web application and VA could improve the accuracy of current clinical decisions tools for TLOC and facilitate clinical stratification processes (such as ensuring appropriate referral to cardiological versus neurological investigation and management pathways).

Objectives/Aims A clinical decision tool for Transient Loss of Consciousness (TLOC) could reduce misdiagnosis rates and waiting times. Most clinical decision tools fail to stratify between the three most common causes of TLOC (epilepsy, functional (dissociative) seizures, and syncope) or are hindered by the challenging differentiation between epilepsy and FDS. Based on previous research describing differences in spoken accounts of epileptic and nonepileptic seizures, this study explored the feasibility of predicting the cause of TLOC by combining the automated analysis of patient-reported symptoms and spoken TLOC descriptions. Method Participants completed an online web application that consisted of a 34-item medical history and symptom questionnaire (iPEP) and interaction with a virtual agent (VA) that asked eight questions about the most recent experience of TLOC. Support Vector Machines (SVM) were trained using different combinations of features and nested leave-one-out cross validation. The iPEP provided a baseline performance. Three language-based feature sets were created: features designed to measure formulation effort, features that measured the proportion of words from different semantic categories, and features based on verb, adverb, and adjective usage. Two methods of integrating the iPEP and language features were compared. Method one involved training a single SVM model using all features and all diagnoses. Method two used a ‘model stacking’ approach whereby predictions of epilepsy or FDS from the iPEP model were passed into a second stage language analysis to improve this differential diagnosis. Results 76 participants completed the application (Epilepsy = 24, FDS = 36, syncope = 16). Only 61 participants also completed the VA interaction (Epilepsy = 20, FDS = 29, syncope = 12). The iPEP model accurately predicted 65.8% of diagnoses. For the binary classification between epilepsy and FDS, the three language feature sets predicted the diagnosis with an accuracy between 75.5–85.7%. Combining the iPEP and language features resulted in an overall accuracy of 59% for the first integration method and 85.5% for method two (model stacking). Conclusion These findings suggest that an automated analysis of TLOC descriptions collected using an online web application and VA could improve the accuracy of current clinical decisions tools for TLOC and facilitate clinical stratification processes (such as the appropriate referral to cardiological versus neurological investigation and management pathways). Future research should aim to improve the baseline performance of the iPEP and explore methods for analysing TLOC descriptions from patients with syncope that can improve the identification of this diagnostic group.

Background: This article uncovers why people with severe expressive aphasia's turns -at-talk are sometimes not treated as producing an action by their communication partners, and the impact this has on the person with aphasia's (PWA's) agency. We demonstrate resources PWAs use to pursue talk and which assist with the production of a recognizable action.Method: We examined turns produced by four PWAs and their communication part-ners (CPs), where present, using conversation analysis, identifying features that do not receive a response and features promoting action ascription. Analysis: The PWAs' semantically empty or unclear turns, turns lacking sequential context, or the CPs' focus on their own actions led to a lack of action ascription. However, CPs do attend to PWAs' multimodal features of interaction, and PWAs' repetition accompanied by an upgraded gesture was shown to pursue a response. Action ascription was aided by the PWAs' preserved use of silence as a communica-tive device. Discussion: When PWAs' actions are not appropriately ascribed, their agency may be diminished. Communication partners should attend to all features of the PWA's turns, including gesture and silence, to progress the PWA's action, rather than their own misappropriated action. This may mean accepting a delay in progressivity while the PWA pursues an appropriate response. Through this, the PWA's agency in inter-action can be maintained, and intersubjectivity achieved.

Background: Asking patients who have been referred to memory clinics open questions about recent events has been shown to have diagnostic relevance. Method: We use conversation analysis to look at responses to questions about recent events. The interviewees are healthy control (HC) participants, people with mild cognitive impairment (MCI), and people with Alzheimer’s disease (AD). Results: We show differences among the groups’ use of claims of memory problems, self-directed questions, and well-prefacing. Healthy control participants produce more talk in response to all of these, while people with MCI and AD either do not, or do so in demonstrably different ways from both HC participants and each other. Discussion/conclusion: Healthy control participants are both willing and able to ‘show off’ their memory, while people with AD are willing but generally unable to do so. People with MCI, in contrast, display themselves as both unwilling and unable to engage with the agent’s questions as tests of memory.

The common causes of Transient Loss of Consciousness (TLOC) are syncope, epilepsy, and functional/dissociative seizures (FDS). Simple, questionnaire-based decision-making tools for non-specialists who may have to deal with TLOC (such as clinicians working in primary or emergency care) reliably differentiate between patients who have experienced syncope and those who have had one or more seizures but are more limited in their ability to differentiate between epileptic seizures and FDS. Previous conversation analysis research has demonstrated that qualitative expert analysis of how people talk to clinicians about their seizures can help distinguish between these two TLOC causes. This paper investigates whether automated language analysis - using semantic categories measured by the Linguistic Inquiry and Word Count (LIWC) toolkit - can contribute to the distinction between epilepsy and FDS. Using patient-only talk manually transcribed from recordings of 58 routine doctor-patient clinic interactions, we compared the word frequencies for 21 semantic categories and explored the predictive performance of these categories using 5 different machine learning algorithms. Machine learning algorithms trained using the chosen semantic categories and leave-one-out cross-validation were able to predict the diagnosis with an accuracy of up to 81%. The results of this proof of principle study suggest that the analysis of semantic variables in seizure descriptions could improve clinical decision tools for patients presenting with TLOC.

IntroductionRecent years have seen an almost sevenfold rise in referrals to specialist memory clinics. This has been associated with an increased proportion of patients referred with functional cognitive disorder (FCD), that is, non-progressive cognitive complaints. These patients are likely to benefit from a range of interventions (eg, psychotherapy) distinct from the requirements of patients with neurodegenerative cognitive disorders. We have developed a fully automated system, ‘CognoSpeak’, which enables risk stratification at the primary–secondary care interface and ongoing monitoring of patients with memory concerns.MethodsWe recruited 15 participants to each of four groups: Alzheimer’s disease (AD), mild cognitive impairment (MCI), FCD and healthy controls. Participants responded to 12 questions posed by a computer-presented talking head. Automatic analysis of the audio and speech data involved speaker segmentation, automatic speech recognition and machine learning classification.ResultsCognoSpeak could distinguish between participants in the AD or MCI groups and those in the FCD or healthy control groups with a sensitivity of 86.7%. Patients with MCI were identified with a sensitivity of 80%.DiscussionOur fully automated system achieved levels of accuracy comparable to currently available, manually administered assessments. Greater accuracy should be achievable through further system training with a greater number of users, the inclusion of verbal fluency tasks and repeat assessments. The current data supports CognoSpeak’s promise as a screening and monitoring tool for patients with MCI. Pending confirmation of these findings, it may allow clinicians to offer patients at low risk of dementia earlier reassurance and relieve pressures on specialist memory services.

Background There are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished from the other two causes relatively easily with a small number of yes/no questions, the differentiation of the other two causes of TLOC is more challenging. Previous qualitative research based on the methodology of Conversation Analysis has demonstrated that epileptic and nonepileptic seizures are described differently when patients talk to clinicians about their TLOC experiences. One particularly prominent difference is that epileptic seizure descriptions are characterised by more formulation effort than accounts of nonepileptic seizures. Aim This research investigates whether features likely to reflect the level of formulation effort can be automatically elicited from audio recordings and transcripts of speech and used to differentiate between epileptic and nonepileptic seizures. Method Verbatim transcripts of conversations between patients and neurologists were manually produced from video and audio recordings of interactions with 45 patients (21 epilepsy and24 PNES). The subsection of each transcript containing the patients account of their first seizure was manually extracted for the analysis. Seven automatically detectable features were designed as markers of formulation effort. These features were used to train a Random Forest machine learning classifier. Results There were significantly more hesitations and repetitions in descriptions of first epileptic than nonepileptic seizures. Using a nested leave-one-out cross validation approach, 71% of seizures were correctly classified by the Random Forest classifier. Conclusions This pilot study provides proof of principle that linguistic features that have been automatically extracted from audio recordings and transcripts could be used to distinguish between epileptic seizures and PNES and thereby contribute to the differential diagnosis of TLOC. Future research should explore whether additional observations can be incorporated into a diagnostic stratification tool. Moreover, future research should explore the performance of these features when they have been extracted from transcripts produced by automatic speech recognition and when they are combined with additional information provided by patients and witnesses about seizure manifestations and medical history.

The early symptoms of neurodegenerative disorders, such as, Alzheimer’s dementia, frequently co-exist with symptoms of depression and anxiety. This phenomenon makes detecting dementia more difficult due to overlapping symptoms. Recent research has shown promising results on the automatic detection of depression and memory problems using features extracted from a person’s speech and language. In this paper, we present the first study of how automatic methods for predicting standardised depression and anxiety scores (PHQ-9 and GAD-7) perform on people presenting with memory problems. We used several regressors and classifiers to predict the scores according to a defined level of score ranges. Feature extraction, feature elimination and usual k-fold training were used. The results show that Recursive Feature Elimination can enhance the accuracy of the correlation coefficients and minimise regression errors. Furthermore, classifying the severity score levels for the lower bands achieved better results than the higher ones.

Objective There are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished relatively easily with a small number of "yes"/"no" questions, the differentiation of the other two causes of TLOC is more challenging. Previous qualitative research based on the methodology of Conversation Analysis has demonstrated that the descriptions of epileptic seizures contain more formulation effort than accounts of PNES. This research investigates whether features likely to reflect the level of formulation effort can be automatically elicited from audio recordings and transcripts of speech and used to differentiate between epileptic and nonepileptic seizures. Method Verbatim transcripts of conversations between patients and neurologists were manually produced from video and audio recordings of 45 interactions (21 epilepsy and 24 PNES). The subsection of each transcript containing the person's account of their first seizure was manually extracted for the analysis. Seven automatically detectable features were designed as markers of formulation effort. These features were used to train a Random Forest machine learning classifier. Result There were significantly more hesitations and repetitions in descriptions of epileptic than nonepileptic seizures. Using a nested leave-one-out cross validation approach, 71% of seizures were correctly classified by the Random Forest classifier. Discussion This pilot study provides proof of principle that linguistic features that have been automatically extracted from audio recordings and transcripts could be used to distinguish between epileptic seizures and PNES and thereby contribute to the differential diagnosis of TLOC. Future research should explore whether additional observations can be incorporated into a diagnostic stratification tool and compare the performance of these features when they are combined with additional information provided by patients and witnesses about seizure manifestations and medical history.

A body of research has shown that there are linguistic differences in the way people with epilepsy talk about their seizures when compared to those with non-epileptic seizures. We extend this line of research by presenting the results of a phonetic analysis comparing speech samples from people with a confirmed diagnosis of epilepsy (7 patients), to those with a confirmed diagnosis of non-epileptic seizures (8 patients). Variables considered include features of pitch, intensity, duration and pausing in their responses to questions from a neurologist during medical history-taking. We find only limited evidence of differences between the two diagnostic groups (epilepsy vs. non-epileptic seizures). We discuss possible reasons for this lack of evidence.

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.

Early detection of cognitive impairment is of great clinical importance. Current cognitive tests assess language and speech abilities. Recently, we have developed a fully automated system to detect cognitive impairment from the analysis of conversations between a person and an intelligent virtual agent (IVA). Promising results have been achieved, however more data than is typically available in the medical domain is required to train more complex classifiers. Data augmentation using generative models has been demonstrated to be an effective approach. In this paper, we use a variational autoencoder to augment data at the feature-level as opposed to the speech signal-level. We investigate whether this suits some feature types (e.g., acoustic, linguistic) better than others. We evaluate the approach on IVA recordings of people with four different cognitive impairment conditions. F-scores of a four-way logistic regression (LR) classifier are improved for certain feature types. For a deep neural network (DNN) classifier, the improvement is seen for almost all feature types. The F-score of the LR classifier on the combined features increases from 55% to 60%, and for the DNN classifier from 49% to 62%. Further improvements are gained by feature selection: 88% and 80% F-scores for LR and DNN classifiers respectively.

Speech-based automatic approaches for detecting neuro-degenerative disorders (ND) and mild cognitive impairment (MCI) have received more attention recently due to being non-invasive and potentially more sensitive than current pen-and-paper tests. The performance of such systems is highly dependent on the choice of features in the classification pipeline. In particular for acoustic features, arriving at a consensus for a best feature set has proven challenging. This paper explores using deep neural network for extracting features directly from the speech signal as a solution to this. Compared with hand-crafted features, more information is present in the raw waveform, but the feature extraction process becomes more complex and less interpretable which is often undesirable in medical domains. Using a SincNet as a first layer allows for some analysis of learned features. We propose and evaluate the Sinc-CLA (with SincNet, Convolutional, Long Short-Term Memory and Attention layers) as a task-driven acoustic feature extractor for classifying MCI, ND and healthy controls (HC). Experiments are carried out on an inhouse dataset. Compared with the popular hand-crafted feature sets, the learned task-driven features achieve a superior classification accuracy. The filters of the SincNet is inspected and acoustic differences between HC, MCI and ND are found.

Objectives/AimsWe used our automated cognitive assessment tool to explore whether responses to questions probing recent and remote memory could aid in distinguishing between patients with early neurodegenerative disorders and those with Functional Cognitive Disorders (FCD).Hypotheses: pwFCD would have no significant differences in pause to speech ratio and measures of linguistic complexity compared to healthy controls. pwFCD would have significant differences in pause to speech ratio and measures of linguistic complexity compared to pwMCI and pwAD.MethodsWe recruited 15 participants with FCD, MCI and AD each as well as 15 healthy controls. Participants answered 12 questions posed by the ‘Digital Doctor’. Automatic processing of the audio-recorded answers involved automatic speech recognition including detecting length of pauses. Two questions probe recent memory, exploring knowledge of current affairs. Two probe remote memory, asking for autobiographical details.We analysed the data using: Pause to speech time ratio. Moving average type token ratio (MATTR): An automated measure of vocabulary richness. Computerised propositional idea density rater (CPIDR): An automated measure of propositional idea density.ResultsThere was a significant difference in the pause to speech ratio for recent memory questions for HC versus AD (P=0.0012) and MCI (p<0.0001) but also compared to those with FCD (p=0.0128). There was a significant difference in the pause to speech ratio for remote memory questions for HC vs AD (p=0.0008) and MCI (p=0.0049) but not FCD (p=0.0613). There was no significant difference between FCD v AD or FCD v MCI. The MATTR and CPIDR were similar across all groups but highest in HC and FMD.ConclusionsThis study rejects both hypotheses. However, the data supports the application of linguistic measures to recent and remote memory questions in distinguishing those with MCI & AD from HC’s. Further work will investigate the utility of incorporating additional measures of lexical and grammatical complexity (word frequency, sentence structure). Longitudinal study will provide insights into which features may predict stability in FCD and HC’s and progression from MCI to AD, supporting the system’s promise as a monitoring tool.

Data limitation is one of the most common issues in training machine learning classifiers for medical applications. Due to ethical concerns and data privacy, the number of people that can be recruited to such experiments is generally smaller than the number of participants contributing to non-healthcare datasets. Recent research showed that generative models can be used as an effective approach for data augmentation, which can ultimately help to train more robust classifiers sparse data domains. A number of studies proved that this data augmentation technique works for image and audio data sets. In this paper, we investigate the application of a similar approach to different types of speech and audio-based features extracted from interactions recorded with our automatic dementia detection system. Using two generative models we show how the generated synthesized samples can improve the performance of a DNN based classifier. The variational autoencoder increased the F-score of a four-way classifier distinguishing the typical patient groups seen in memory clinics from 58% to around 74%, a 16% improvement.

The ageing population has caused a marked increased in the number of people with cognitive decline linked with dementia. Thus, current diagnostic services are overstretched, and there is an urgent need for automating parts of the assessment process. In previous work, we demonstrated how a stratification tool built around an Intelligent Virtual Agent (IVA) eliciting a conversation by asking memory-probing questions, was able to accurately distinguish between people with a neuro-degenerative disorder (ND) and a functional memory disorder (FMD). In this paper, we extend the number of diagnostic classes to include healthy elderly controls (HCs) as well as people with mild cognitive impairment (MCI). We also investigate whether the IVA may be used for administering more standard cognitive tests, like the verbal fluency tests. A four-way classifier trained on an extended feature set achieved 48% accuracy, which improved to 62% by using just the 22 most significant features (ROC-AUC: 82%).