An analysis-by-synthesis approach to vocal tract modeling for robust speech recognition

In this thesis we present a novel approach to speech recognition that incorporates knowledge of the speech production process. The major contribution is the development of a speech recognition system that is motivated by the physical generative process of speech, rather than the purely statistical approach that has been the basis for virtually all current recognizers. We follow an analysis-by-synthesis approach. We begin by attributing a physical meaning to the inner states of the recognition system pertaining to the configurations the human vocal tract takes over time. We utilize a geometric model of the vocal tract, adapt it to our speakers, and derive realistic vocal tract shapes from electromagnetic articulograph (EMA) measurements in the MOCHA database. We then synthesize speech from the vocal tract configurations using a physiologically-motivated articulatory synthesis model of speech generation. Finally, the observation probability of the Hidden Markov Model (HMM) used for phone classification is a function of the distortion between the speech synthesized from the vocal tract configurations and the real speech. The output of each state in the HMM is based on a mixture of density functions

Anxiety and depressive symptoms are associated with poor sleep health during a period of COVID-19-induced nationwide lockdown: a cross-sectional analysis of adults in Jordan.

BACKGROUND: Jordan, a Middle Eastern country, declared a state of national emergency due to COVID-19 and a strict nationwide lockdown on 17 March 2020, banning all travel and movement around the country, potentially impacting mental health. This study sought to investigate the association between mental health (eg, anxiety and depressive symptoms) and sleep health among a sample of Jordanians living through a state of COVID-19-induced nationwide lockdown. METHODS: Using Facebook, participants (n=1240) in Jordan in March 2020 were recruited and direct to a web-based survey measuring anxiety (items from General Anxiety Disorder 7-item (GAD-7) scale instrument), depressive symptoms (items from Center for Epidemiologic Studies Depression Scale), sleep health (items from the Pittsburgh Sleep Quality Index) and sociodemographic. A modified Poisson regression model with robust error variance. Adjusted prevalence ratios (aPRs) and 95% CIs were estimated to examine how anxiety and depressive symptoms may affect different dimensions of sleep health: (1) poor sleep quality, (2) short sleep duration, (3) encountering sleep problems. RESULTS: The majority of participants reported having experienced mild (33.8%), moderate (12.9%) or severe (6.3%) levels of anxiety during lockdown, and nearly half of respondents reported depressive symptoms during lockdown. Similarly, over 60% of participants reported having experienced at least one sleep problem in the last week, and nearly half reported having had short sleep duration. Importantly, anxiety was associated with poor sleep health outcomes. For example, corresponding to the dose-response relationship between anxiety and sleep health outcomes, those reporting severe anxiety were the most likely to experience poor sleep quality (aPR =8.95; 95% CI=6.12 to 13.08), short sleep duration (aPR =2.23; 95% CI=1.91 to 2.61) and at least one problem sleep problem (aPR=1.73; 95% CI=1.54 to 1.95). Moreover, depressive symptoms were also associated with poor sleep health outcomes. As compared with scoring in the first quartile, scoring fourth quartile was associated with poor sleep quality (aPR=11.82; 95% CI=6.64 to 21.04), short sleep duration (aPR=1.87; 95% CI=1.58 to 2.22), and experiencing at least one sleep problem (aPR=1.90; 95% CI=1.66 to 2.18). CONCLUSIONS: Increased levels of anxiety and depressive symptoms can negatively influence sleep health among a sample of Jordanian adults living in a state of COVID-19-induced nationwide lockdown

Modeling of oropharyngeal articulatory adaptation to compensate for the acoustic effects of nasalization

Hypernasality is one of the most detrimental speech disturbances that lead to declines of speech intelligibility. Velopharyngeal inadequacy, which is associated with anatomic defects such as cleft palate or neuromuscular disorders that affect velopharygneal function, is the primary cause of hypernasality. A simulation study by Rong and Kuehn [J. Speech Lang. Hear. Res. 55(5), 1438–1448 (2012)] demonstrated that properly adjusted oropharyngeal articulation can reduce nasality for vowels synthesized with an articulatory model [Mermelstein, J. Acoust. Soc. Am. 53(4), 1070–1082 (1973)]. In this study, a speaker-adaptive articulatory model was developed to simulate speaker-customized oropharyngeal articulatory adaptation to compensate for the acoustic effects of nasalization on /a/, /i/, and /u/. The results demonstrated that (1) the oropharyngeal articulatory adaptation effectively counteracted the effects of nasalization on the second lowest formant frequency (F2) and partially compensated for the effects of nasalization on vowel space (e.g., shifting and constriction of vowel space) and (2) the articulatory adaptation strategies generated by the speaker-adaptive model might be more efficacious for counteracting the acoustic effects of nasalization compared to the adaptation strategies generated by the standard articulatory model in Rong and Kuehn. The findings of this study indicated the potential of using oropharyngeal articulatory adaptation as a means to correct maladaptive articulatory behaviors and to reduce nasalit

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers

The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease

Phenolic Compounds Removal from Olive Mill Wastewater Using the Composite of Activated Carbon and Copper-Based Metal-Organic Framework

As the industry of olive oil continues to grow, the management of olive mill wastewater (OMW) by-products has become an area of great interest. While many strategies for processing OMW have been established, more studies are still required to find an effective adsorbent for total phenolic content uptake. Here, we present a composite of a Cu 1,4-benzene dicarboxylate metal-organic framework (Cu (BDC) MOF) and granular activated carbon (GAC) as an adsorbent for total phenolic content removal from OMW. Experimental results demonstrated that the maximum adsorption capacity was 20 mg/g of total phenolic content (TPC) after 4 h. using 2% wt/wt of GAC/Cu (BDC) MOF composite to OMW at optimum conditions (pH of 4.0 and 25 °C). The adsorption of phenolic content onto the GAC/Cu (BDC) MOF composite was described by the Freundlich adsorption and pseudo-second-order reaction. The adsorption reaction was found to be spontaneous and endothermic at 298 K where ΔS° and ΔH° were found to be 0.105 KJ/mol and 25.7 kJ/mol, respectively. While ΔGº value was −5.74 (kJ/mol). The results of this study provide a potential solution for the local and worldwide olive oil industry

A Review of the Recent Advances in Alzheimer’s Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics

Alzheimer’s disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer’s disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer’s disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments

Anxiety and depressive symptoms are associated with poor sleep health during a period of COVID-19-induced nationwide lockdown: a cross-sectional analysis of adults in Jordan

Background Jordan, a Middle Eastern country, declared a state of national emergency due to COVID-19 and a strict nationwide lockdown on 17 March 2020, banning all travel and movement around the country, potentially impacting mental health. This study sought to investigate the association between mental health (eg, anxiety and depressive symptoms) and sleep health among a sample of Jordanians living through a state of COVID-19-induced nationwide lockdown.Methods Using Facebook, participants (n=1240) in Jordan in March 2020 were recruited and direct to a web-based survey measuring anxiety (items from General Anxiety Disorder 7-item (GAD-7) scale instrument), depressive symptoms (items from Center for Epidemiologic Studies Depression Scale), sleep health (items from the Pittsburgh Sleep Quality Index) and sociodemographic. A modified Poisson regression model with robust error variance. Adjusted prevalence ratios (aPRs) and 95% CIs were estimated to examine how anxiety and depressive symptoms may affect different dimensions of sleep health: (1) poor sleep quality, (2) short sleep duration, (3) encountering sleep problems.Results The majority of participants reported having experienced mild (33.8%), moderate (12.9%) or severe (6.3%) levels of anxiety during lockdown, and nearly half of respondents reported depressive symptoms during lockdown. Similarly, over 60% of participants reported having experienced at least one sleep problem in the last week, and nearly half reported having had short sleep duration. Importantly, anxiety was associated with poor sleep health outcomes. For example, corresponding to the dose–response relationship between anxiety and sleep health outcomes, those reporting severe anxiety were the most likely to experience poor sleep quality (aPR =8.95; 95% CI=6.12 to 13.08), short sleep duration (aPR =2.23; 95% CI=1.91 to 2.61) and at least one problem sleep problem (aPR=1.73; 95% CI=1.54 to 1.95). Moreover, depressive symptoms were also associated with poor sleep health outcomes. As compared with scoring in the first quartile, scoring fourth quartile was associated with poor sleep quality (aPR=11.82; 95% CI=6.64 to 21.04), short sleep duration (aPR=1.87; 95% CI=1.58 to 2.22), and experiencing at least one sleep problem (aPR=1.90; 95% CI=1.66 to 2.18).Conclusions Increased levels of anxiety and depressive symptoms can negatively influence sleep health among a sample of Jordanian adults living in a state of COVID-19-induced nationwide lockdown