Digital signal processing algorithms for automatic voice recognition

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms

Automatic voice recognition using traditional and artificial neural network approaches

The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time

Patterns of residual stresses due to welding

Residual stresses caused by welding result from the nonuniform rate of cooling and the restrained thermal contraction or non-uniform plastic deformation. From the zone of extremely high temperature at the weld, heat flows into both the adjoining cool body and the surrounding atmosphere. The weld metal solidifies under very rapid cooling. The plasticity of the hot metal allows adjustment initially, but as the structure cools the rigidity of the surrounding cold metal inhibits further contraction. The zone is compressed and the weld is put under tensile stresses of high magnitude. The danger of cracking in these structural elements is great. Change in specific volume is caused by the change in temperature

Praziquantel: its use in control of schistosomiasis in sub-Saharan Africa and current research needs

Treatment with praziquantel (PZQ) has become virtually the sole basis of schistosomiasis control in sub-Saharan Africa and elsewhere, and the drug is reviewed here in the context of the increasing rate that it is being used for this purpose. Attention is drawn to our relative lack of knowledge about the mechanisms of action of PZQ at the molecular level, the need for more work to be done on schistosome isolates that have been collected recently from endemic areas rather than those maintained in laboratory conditions for long periods, and our reliance for experimental work mainly on Schistosoma mansoni, little work having been done on S. haematobium. There is no evidence that resistance to PZQ has been induced in African schistosomes as a result of its large-scale use on that continent to date, but there is also no assurance that PZQ and/or schistosomes are in any way unique and that resistant organisms will not be selected as a result of widespread drug usage. The failure of PZQ to produce complete cures in populations given a routine treatment should therefore solicit considerable concern. With few alternatives to PZQ currently available and/or on the horizon, methods to monitor drug-susceptibility in African schistosomes need to be devised and used to help ensure that this drug remains effective for as long a time as possibl

Determining the Impact of Demographic Factors on Adherence to Glaucoma Treatment in Patients of African Descent

poster abstractPrimary open-angle glaucoma (POAG) affects approximately 2.5 million Americans. Elevated intraocular pressure (IOP) is the only treatable risk factor to slow the progression of the disease and prevent blindness. Topical ocular hypotensive medications, dispensed in the form of eye drops, are the first line of treatment to reduce IOP. Patients are required to use their eye drops once or twice daily throughout the rest of their lives. Patients of African descent are more vulnerable to this chronic disease, with a prevalence six times higher than patients of European descent. They also have worse adherence to the treatment regimen in general. The main purpose of this study was to determine the impact of education, age, gender, household income, marital status, employment and number of prescribed medications on the adherence to the glaucoma treatment. Twenty-one patients were included and adherence was measured using Medication Event Monitoring System caps, which electronically record every time a patient uses their eye drops. After 4 weeks, patients returned with the caps and the compliance level was recorded. During the initial interview, patients answered a questionnaire about the different factors tested in this study. There was a positive correlation between the compliance percentage and age, with patients who are 70 years or older having the highest compliance levels (82% compared to 62% in the 50s and 60s category). Education also affected compliance, with patients who have a high school degree having a lower compliance at 62% compared to the patients with some college or a bachelor’s degree with compliance of 81%. The employment status was another contributor, with higher compliance in full-time employed patients compared to other employment types. The remaining factors did not contribute to the adherence levels. Overall, education, age, and employment status were the only factors that impacted adherence levels

Is There an Association Between Oral Health and Severity of COVID-19 Complications?

The new coronavirus SARS-CoV-2 was first detected in late 2019 and has quickly developed into a global pandemic [1]. Age is one of the highest risk factors for developing severe symptoms of COVID-19, the disease caused by infection with SARS-CoV-2 [2]. Thus, individuals over the age of 65 and those living in long-term care facilities are especially vulnerable to morbidity and mortality due to infection with SARS-CoV-2. However, persons with chronic lung disease, moderate to severe asthma, severe obesity, diabetes, chronic kidney disease, and liver disease are also at high risk for severe COVID-19 symptoms. A recent study lists hypertension, obesity, and diabetes as the three major underlying conditions with the most unfavorable outcomes in COVID-19 patients requiring hospitalization [3]. While COVID-19 can affect multiple organs in the body, including the kidneys and liver [4, 5], the main cause of mortality is due to the ability of SARS-CoV-2 to infect the respiratory tract, leading to severe pneumonia. Patients with COVID-19 display symptoms of fever, cough, dyspnea, and other complications associated with acute respiratory distress syndrome [6-8]. A salient feature of COVID-19 is its ability to trigger an excessive immune reaction in the host, termed a ‘cytokine storm’, which causes extensive tissue damage, particularly in the connective tissue of the lungs [9]. The lung pathology of patients who die from COVID-19 pneumonia includes edema, focal reactive hyperplasia of pneumocytes with patchy inflammatory cellular infiltration, and multinucleated giant cells [10]

Hybrid Method for Digits Recognition using Fixed-Frame Scores and Derived Pitch

This paper presents a procedure of frame normalization based on the traditional dynamic time warping (DTW) using the LPC coefficients. The redefined method is called as the DTW frame-fixing method (DTW-FF), it works by normalizing the word frames of the input against the reference frames. The enthusiasm to this study is due to neural network limitation that entails a fix number of input nodes for when processing multiple inputs in parallel. Due to this problem, this research is initiated to reduce the amount of computation and complexity in a neural network by reducing the number of inputs into the network. In this study, dynamic warping process is used, in which local distance scores of the warping path are fixed and collected so that their scores are of equal number of frames. Also studied in this paper is the consideration of pitch as a contributing feature to the speech recognition. Results showed a good performance and improvement when using pitch along with DTW-FF feature. The convergence rate between using the steepest gradient descent is also compared to another method namely conjugate gradient method. Convergence rate is also improved when conjugate gradient method is introduced in the back-propagation algorithm