Perplexity – a new predictor of cognitive changes in spoken language? – results of the Interdisciplinary Longitudinal Study on Adult Development and Aging (ILSE)

Abstract: In addition to memory loss, progressive deterioration of speech and language skills is among the main symptoms at the onset of Alzheimer’s disease (AD) as well as in mild cognitive impairment (MCI). Detailed interview analyses demonstrated early symptoms years before the onset of AD/MCI. Automatic speech processing could be a promising approach to identifying underlying mechanisms in larger studies or even support diagnostics. Perplexity as a measure of predictability of text could be a sensitive indicator of cognitive deterioration. Therefore, voice recordings from the Interdisciplinary Longitudinal Study on Adult Development and Aging were analyzed with regard to neuropsychological parameters in participants that develop MCI/AD or remain cognitively healthy. Preliminary results indicate that perplexity predicts severity of cognitive deficits and information processing speed obtained 10–12 years later in participants who developed MCI/AD in contrast to those who stayed healthy. Findings support the heuristic value of research on the diagnostic potential of automatic speech processing

How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease.

Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability

Temporal Integration of Text Transcripts and Acoustic Features for Alzheimer's Diagnosis Based on Spontaneous Speech

Background: Advances in machine learning (ML) technology have opened new avenues for detection and monitoring of cognitive decline. In this study, a multimodal approach to Alzheimer's dementia detection based on the patient's spontaneous speech is presented. This approach was tested on a standard, publicly available Alzheimer's speech dataset for comparability. The data comprise voice samples from 156 participants (1:1 ratio of Alzheimer's to control), matched by age and gender. Materials and Methods: A recently developed Active Data Representation (ADR) technique for voice processing was employed as a framework for fusion of acoustic and textual features at sentence and word level. Temporal aspects of textual features were investigated in conjunction with acoustic features in order to shed light on the temporal interplay between paralinguistic (acoustic) and linguistic (textual) aspects of Alzheimer's speech. Combinations between several configurations of ADR features and more traditional bag-of-n-grams approaches were used in an ensemble of classifiers built and evaluated on a standardised dataset containing recorded speech of scene descriptions and textual transcripts. Results: Employing only semantic bag-of-n-grams features, an accuracy of 89.58% was achieved in distinguishing between Alzheimer's patients and healthy controls. Adding temporal and structural information by combining bag-of-n-grams features with ADR audio/textual features, the accuracy could be improved to 91.67% on the test set. An accuracy of 93.75% was achieved through late fusion of the three best feature configurations, which corresponds to a 4.7% improvement over the best result reported in the literature for this dataset. Conclusion: The proposed combination of ADR audio and textual features is capable of successfully modelling temporal aspects of the data. The machine learning approach toward dementia detection achieves best performance when ADR features are combined with strong semantic bag-of-n-grams features. This combination leads to state-of-the-art performance on the AD classification task

Dementia detection using automatic analysis of conversations

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

Detecting early signs of dementia in conversation

Dementia can affect a person's speech, language and conversational interaction capabilities. The early diagnosis of dementia is of great clinical importance. Recent studies using the qualitative methodology of Conversation Analysis (CA) 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 Neuro-degenerative Disorders (ND) and those with non-progressive Functional Memory Disorder (FMD). However, conducting manual CA is expensive and difficult to scale up for routine clinical use.\ud This study introduces an automatic approach for processing such conversations which can help in identifying the early signs of dementia and distinguishing them from the other clinical categories (FMD, Mild Cognitive Impairment (MCI), and Healthy Control (HC)). The dementia detection system starts with a speaker diarisation module to segment an input audio file (determining who talks when). Then the segmented files are passed to an automatic speech recogniser (ASR) to transcribe the utterances of each speaker. Next, the feature extraction unit extracts a number of features (CA-inspired, acoustic, lexical and word vector) from the transcripts and audio files. Finally, a classifier is trained by the features to determine the clinical category of the input conversation. Moreover, we investigate replacing the role of a neurologist in the conversation with an Intelligent Virtual Agent (IVA) (asking similar questions). We show that despite differences between the IVA-led and the neurologist-led conversations, the results achieved by the IVA are as good as those gained by the neurologists. Furthermore, the IVA can be used for administering more standard cognitive tests, like the verbal fluency tests and produce automatic scores, which then can boost the performance of the classifier. The final blind evaluation of the system shows that the classifier can identify early signs of dementia with an acceptable level of accuracy and robustness (considering both sensitivity and specificity)