A Multi-Modal Feature Embedding Approach to Diagnose Alzheimer Disease from Spoken Language

Introduction: Alzheimer's disease is a type of dementia in which early diagnosis plays a major rule in the quality of treatment. Among new works in the diagnosis of Alzheimer's disease, there are many of them analyzing the voice stream acoustically, syntactically or both. The mostly used tools to perform these analysis usually include machine learning techniques. Objective: Designing an automatic machine learning based diagnosis system will help in the procedure of early detection. Also, systems, using noninvasive data are preferable. Methods: We used are classification system based on spoken language. We use three (statistical and neural) approaches to classify audio signals from spoken language into two classes of dementia and control. Result: This work designs a multi-modal feature embedding on the spoken language audio signal using three approaches; N-gram, i-vector, and x-vector. The evaluation of the system is done on the cookie picture description task from Pitt Corpus dementia bank with the accuracy of 83:6Comment: 14 pages, 4 figure

Pathological speech detection using x-vector embeddings

The potential of speech as a non-invasive biomarker to assess a speaker's health has been repeatedly supported by the results of multiple works, for both physical and psychological conditions. Traditional systems for speech-based disease classification have focused on carefully designed knowledge-based features. However, these features may not represent the disease's full symptomatology, and may even overlook its more subtle manifestations. This has prompted researchers to move in the direction of general speaker representations that inherently model symptoms, such as Gaussian Supervectors, i-vectors and, x-vectors. In this work, we focus on the latter, to assess their applicability as a general feature extraction method to the detection of Parkinson's disease (PD) and obstructive sleep apnea (OSA). We test our approach against knowledge-based features and i-vectors, and report results for two European Portuguese corpora, for OSA and PD, as well as for an additional Spanish corpus for PD. Both x-vector and i-vector models were trained with an out-of-domain European Portuguese corpus. Our results show that x-vectors are able to perform better than knowledge-based features in same-language corpora. Moreover, while x-vectors performed similarly to i-vectors in matched conditions, they significantly outperform them when domain-mismatch occurs.Comment: Rejected for publication by peer revie