Exploring Acoustic and Linguistic Features in Alzheimer’s Dementia

Abstract

Dementia is a chronic and progressive syndrome that affects the cognitive functioning of an individual. Alzheimer’s, a neurodegenerative disorder, is the leading cause of dementia. The only way to control the progression of the disease is its early detection, followed by drug and non-drug interventions. The speech production chain is the presentation of cognitive abilities and is majorly affected in the early stages of Alzheimer’s disease. Speech signals are ubiquitous and facilitate easy recording, storage, and transfer. For these reasons, researchers have long strived to develop complementary tools for Alzheimer’s Dementia detection using acoustic and linguistic clues derived from speech utterances. This thesis is one more attempt at finding the differentiating auditory and linguistic clues in the utterance(audio and transcript) of an Alzheimer’s Dementia patient. The ADReSS INTERSPEECH-2020 and ADReSSo INTERSPEECH-2021 challenges provide a balanced dataset for the Alzheimer’s Dementia classification task. This thesis explores the efficacy of different acoustic features to capture distinct patterns in the speech utterance of AD patients. The features are obtained by evaluating Cepstral Coefficients over different acoustic algorithms and techniques. Mel-frequency and Linear Prediction methods are used to capture the Vocal tract characteristics; Residual Coefficient, Glottal Volume Velocity, and Zero Frequency Filtering approach for excitation source characteristics; Envelope Modulation Spectrum and Long Term Averaging Spectrum capture the prosody characteristics of speech utterance. The next part of this thesis explores the Single-frequency-filtering-based(SFF) high spectrotemporal resolution feature using the filter bank approach for Alzheimer’s Dementia detection. Experiments are performed using different machine learning classifiers over the acoustic features extracted from the challenge datasets. The current study also demonstrates the performance of the BERT model for the dementia classification task. Finally, the performance of individual and combined acoustic features is reported. Also, the classification score is evaluated by score level fusion of the acoustic models and BERT model to observe the complementary characteristics of acoustic features to the BERT model. Acoustic models perform best when combined with linguistic features, suggesting the complementary nature of acoustic and linguistic features. Also, the high spectro-temporal resolution Single Frequency Filtering feature captures the characteristics of speech patterns better for Alzheimer’s Dementia classification than traditional source-filter model-based features.