Speech processing with deep learning for voice-based respiratory diagnosis : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand

Voice-based respiratory diagnosis research aims at automatically screening and diagnosing respiratory-related symptoms (e.g., smoking status, COVID-19 infection) from human-generated sounds (e.g., breath, cough, speech). It has the potential to be used as an objective, simple, reliable, and less time-consuming method than traditional biomedical diagnosis methods. In this thesis, we conduct one comprehensive literature review and propose three novel deep learning methods to enrich voice-based respiratory diagnosis research and improve its performance. Firstly, we conduct a comprehensive investigation of the effects of voice features on the detection of smoking status. Secondly, we propose a novel method that uses the combination of both high-level and low-level acoustic features along with deep neural networks for smoking status identification. Thirdly, we investigate various feature extraction/representation methods and propose a SincNet-based CNN method for feature representations to further improve the performance of smoking status identification. To the best of our knowledge, this is the first systemic study that applies speech processing with deep learning for voice-based smoking status identification. Moreover, we propose a novel transfer learning scheme and a task-driven feature representation method for diagnosing respiratory diseases (e.g., COVID-19) from human-generated sounds. We find those transfer learning methods using VGGish, wav2vec 2.0 and PASE+, and our proposed task-driven method Sinc-ResNet have achieved competitive performance compared with other work. The findings of this study provide a new perspective and insights for voice-based respiratory disease diagnosis. The experimental results demonstrate the effectiveness of our proposed methods and show that they have achieved better performances compared to other existing methods

Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis

Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82–86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity

UWOMJ Volume 33

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1006/thumbnail.jp

Medico-Legal Aspects of the Nervous System as a Functioning Unit of the Body

We have had the pleasure of working together in recent years on Law-Science problems. During that time we have become increasingly convinced that it is necessary for trial lawyer and scientist alike to think of the human being in terms of the nine main organ systems,\u27reserving a tenth category for the field of personality as the latter represents a synthesis of component structures and functions into variable reaction and behavior patterns. An injury or disability may involve impairment or destruction of an an atomic member or of physiological function; it may involve effects on personality, or psychic values, alone, without discoverable organic lesion, or it may cause disocations both in organ systems and in over-all personality. To achieve a scientific approach to medico-legal problems one must undertake focal analysis in the discovery, or exploratory phases, and synthesis in the phases of evaluation and prognosis

The Association Between Oral Microorgansims And Aspiration Pneumonia In The Institutionalized Elderly: Review And Recommendations

Aspiration pneumonia is a leading cause of illness and death in persons who reside in long-term-care facilities and, combined with the lack of proper oral health care and services, the risk of aspiration pneumonia rises. The purpose of this article is to review recent literature on oral hygiene and oral care in long-term-care facilities and report new findings regarding associated risks for aspiration pneumonia, as well as research on oral care and health outcomes. The PubMed MeSH database was utilized to direct a specific search by entering terms ‘‘aspiration pneumonia’’ and ‘‘oral hygiene’’ from 1970 to 2009, which yielded 34 articles. The Ovid and Google Scholar databases were utilized as well and provided no additional references for the two terms. A manual search of references from other articles, including three systematic reviews published over the past decade, provided additional information regarding oral microorganisms and respiratory pathogens, as well as investigations of oral care. Finally, a brief but comprehensive introductory review was organized regarding oral microorganisms, biofilm, periodontal disease, and pneumonia to establish a framework for discussion. Over- all, studies suggest (1) an association between poor oral hygiene and respiratory pathogens, (2) a decrease in the incidence of respiratory complications when patients are provided chemical or mechanical interventions for improved oral care, (3) the complex nature of periodontal disease and aspiration pneumonia make direct connections between the two challenging, and (4) additional studies are warranted to determine adequate oral hygiene protocols for nursing home patients to further reduce the incidence of aspiration pneumonia

A comparison of acoustic and linguistics methodologies for Alzheimer’s dementia recognition

In the light of the current COVID-19 pandemic, the need for remote digital health assessment tools is greater than ever. This statement is especially pertinent for elderly and vulnerable populations. In this regard, the INTERSPEECH 2020 Alzheimer’s Dementia Recognition through Spontaneous Speech (ADReSS) Challenge offers competitors the opportunity to develop speech and language-based systems for the task of Alzheimer’s Dementia (AD) recognition. The challenge data consists of speech recordings and their transcripts, the work presented herein is an assessment of different contemporary approaches on these modalities. Specifically, we compared a hierarchical neural network with an attention mechanism trained on linguistic features with three acoustic-based systems: (i) Bag-of-Audio-Words (BoAW) quantising different low-level descriptors, (ii) a Siamese Network trained on log-Mel spectrograms, and (iii) a Convolutional Neural Network (CNN) end-to-end system trained on raw waveforms. Key results indicate the strength of the linguistic approach over the acoustics systems. Our strongest test-set result was achieved using a late fusion combination of BoAW, End-to-End CNN, and hierarchical-attention networks, which outperformed the challenge baseline in both the classification and regression tasks

The Normal Physiological Aspects of Aging as They Relate to Nurse Aid Care

The education of caregivers is essential to proper care. The retention of nurse aids in long-term care of the elderly can be enhanced by specific instruction in the physical aspects of aging as it relates to the required care. The educational level of nurse aids may not give them the perspective needed to interpret the changes they see taking place in the elderly. This paper provides some of that additional perspective. Aging changes begin at the cellular level Cell tasks are specific and functional Aging effects changes in cell function and response with subsequent visible and experienced signs and symptoms of aging. Anatomical and physiological age-related changes are the subsequent result of the overall, ongoing aging process. The body systems are interrelated and interdependent. Skin and hair require a change in the aid care as they become dry and thin. The musculoskeletal ~stem changes result in. a loss of strength and height, and includes some visual changes, all of which require special aid attention. The nervous system changes result in a slowing of the response time to stimuli The endocrine system oversees many of the age related changes. The endocrine system as well as the respiratory and circulatory systems, have few functions that the nurse aid can directly influence, yet the changes require knowledgeable aid care. The digestive and urinary systems are two of the systems most apparently affected as we age and aid knowledge of the changes can be of great assistance to the elderly. The reproductive system slowly undergoes changes in both the male and female. Aid awareness of these changes will yield better care for the elderly

Broadcast speech and the effect of voice quality on the listener : a study of the various components which categorise listener perception by vocal characteristics.

Voice quality is crucial to the art of the broadcast speaker. Acceptable voice quality is a necessity for an acceptable microphone voice and essential therefore for employment as a broadcaster. This thesis investigates the characteristics of the voice which provide that acceptability; and categorises the features which lead the listener to make judgements about their vocal likes and dislikes. These subjective judgements are explored by investigating the psychological, medical, and innate features contributing to the vocal perceptions of the listener. Voice quality is related to the efficiency of the larynx and its importance to voice production; and to the various vocal disorders which can affect the broadcaster. It becomes evident throughout the thesis that each listener receives a clear impression of the personality of the speaker through the features present in the voice. Many of these impressions however are based on stereotypes. The thesis relates these stereotypical judgements to accents, investigating their relationship to the 'BBC' voice, the 'World Service' voice, the 'ILR' voice and the 'reporter's voice' . It is shown that the listener's subjective impression of the voice and the broadcaster personality is formed by the presentational and physical aspects of voice quality. Listener perceptions of voice acceptability are tested and discussed. The data is analysed to provide a set of dominant characteristics from which are drawn voice histograms and frequency polygons. The result is a set of preferred voice characteristics which apply specifically to the broadcast speaker and which can be sought during the selection process