Audio-Visual Automatic Speech Recognition Using PZM, MFCC and Statistical Analysis

Audio-Visual Automatic Speech Recognition (AV-ASR) has become the most promising research area when the audio signal gets corrupted by noise. The main objective of this paper is to select the important and discriminative audio and visual speech features to recognize audio-visual speech. This paper proposes Pseudo Zernike Moment (PZM) and feature selection method for audio-visual speech recognition. Visual information is captured from the lip contour and computes the moments for lip reading. We have extracted 19th order of Mel Frequency Cepstral Coefficients (MFCC) as speech features from audio. Since all the 19 speech features are not equally important, therefore, feature selection algorithms are used to select the most efficient features. The various statistical algorithm such as Analysis of Variance (ANOVA), Kruskal-wallis, and Friedman test are employed to analyze the significance of features along with Incremental Feature Selection (IFS) technique. Statistical analysis is used to analyze the statistical significance of the speech features and after that IFS is used to select the speech feature subset. Furthermore, multiclass Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naive Bayes (NB) machine learning techniques are used to recognize the speech for both the audio and visual modalities. Based on the recognition rate combined decision is taken from the two individual recognition systems. This paper compares the result achieved by the proposed model and the existing model for both audio and visual speech recognition. Zernike Moment (ZM) is compared with PZM and shows that our proposed model using PZM extracts better discriminative features for visual speech recognition. This study also proves that audio feature selection using statistical analysis outperforms methods without any feature selection technique

Health Status among Biscuit Factory Workers in Greater Noida, Uttar Pradesh: A Cross Sectional Study

Background: Today the trend in all countries is towards industrialization. As industries are developing, occupational diseases are becoming more prominent. Purpose: The purpose of this study is to see the effect of different environmental hazards like sugar dust, flour dust, vibratory sieves, effect of ammonia, skin diseases of oven operators, cuts and burns at mixing section, effect of noises at mixers, compressors, generators, blowers of ovens etc. on the health status of the biscuit factory workers. Objective of the Study: To study the socio-demographic and health profile of the employees and to find the association between the various hazards and risk factors. Materials and Methods: A cross sectional study was done among the factory workers of Anmol Biscuit factory, Greater Noida, Uttar Pradesh. A sample size of 250 was taken by simple randomization. Duration of study was for one month from 1st June to 1st July, 2014. Pre validated semi open ended questionnaire was used for data collection. The performa included information about socio demographic profile, present health status, nutritional status, general physical examination, anthropometric measurements and systematic examination.Results: Among all the occupational hazards, thermal hazard was the most common (20%) and association between thermal hazards and use of protective equipment is statistically significant (Chi-square 0.029, P value 0.05 at 2 d.f.).Conclusion: Most of the biscuit factory authorities do not invest much in the safety measures and protective equipment for their workers. These short term benefits might prove counterproductive in a long term

Audio-Visual Automatic Speech Recognition Towards Education for Disabilities

Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition

A comparative study of interaction of tetracycline with several proteins using time resolved anisotropy, phosphorescence, docking and FRET

A comparative study of the interaction of an antibiotic Tetracycline hydrochloride (TC) with two albumins, Human serum albumin (HSA) and Bovine serum albumin (BSA) along with Escherichia Coli Alkaline Phosphatase (AP) has been presented exploiting the enhanced emission and anisotropy of the bound drug. The association constant at 298 K is found to be two orders of magnitude lower in BSA/HSA compared to that in AP with number of binding site being one in each case. Fluorescence resonance energy transfer (FRET) and molecular docking studies have been employed for the systems containing HSA and BSA to find out the particular tryptophan (Trp) residue and the other residues in the proteins involved in the binding process. Rotational correlation time (θc) of the bound TC obtained from time resolved anisotropy of TC in all the protein-TC complexes has been compared to understand the binding mechanism. Low temperature (77 K) phosphorescence (LTP) spectra of Trp residues in the free proteins (HSA/BSA) and in the complexes of HSA/BSA have been used to specify the role of Trp residues in FRET and in the binding process. The results have been compared with those obtained for the complex of AP with TC. The photophysical behaviour (viz., emission maximum, quantum yield, lifetime and θc) of TC in various protic and aprotic polar solvents has been determined to address the nature of the microenvironment of TC in the protein-drug complexes

Post-transcriptional microRNA repression of PMP22 dose in severe Charcot-Marie-Tooth disease type 1

Copy number variation (CNV) may lead to pathological traits, and Charcot-Marie-Tooth disease type 1A (CMT1A), the commonest inherited peripheral neuropathy, is due to a genomic duplication encompassing the dosage-sensitive PMP22 gene. MicroRNAs act as repressors on post-transcriptional regulation of gene expression and in rodent models of CMT1A, overexpression of one such microRNA (miR-29a) has been shown to reduce the PMP22 transcript and protein level. Here we present genomic and functional evidence, for the first time in a human CNV-associated phenotype, of the 3' untranslated region (3'-UTR)-mediated role of microRNA repression on gene expression. The proband of the family presented with an early-onset, severe sensorimotor demyelinating neuropathy and harboured a novel de novo deletion in the PMP22 3'-UTR. The deletion is predicted to include the miR-29a seed binding site and transcript analysis of dermal myelinated nerve fibres using a novel platform, revealed a marked increase in PMP22 transcript levels. Functional evidence from Schwann cell lines harbouring the wildtype and mutant 3'-UTR showed significantly increased reporter assay activity in the latter which was not ameliorated by overexpression of a miR-29a mimic. This shows the importance of miR-29a in regulating PMP22 expression and opens an avenue for therapeutic drug development

Heterozygous frameshift variants in HNRNPA2B1 cause early-onset oculopharyngeal muscular dystrophy

Missense variants in RNA-binding proteins (RBPs) underlie a spectrum of disease phenotypes, including amyotrophic lateral sclerosis, frontotemporal dementia, and inclusion body myopathy. Here, we present ten independent families with a severe, progressive muscular dystrophy, reminiscent of oculopharyngeal muscular dystrophy (OPMD) but of much earlier onset, caused by heterozygous frameshift variants in the RBP hnRNPA2/B1. All disease-causing frameshift mutations abolish the native stop codon and extend the reading frame, creating novel transcripts that escape nonsense-mediated decay and are translated to produce hnRNPA2/B1 protein with the same neomorphic C-terminal sequence. In contrast to previously reported disease-causing missense variants in HNRNPA2B1, these frameshift variants do not increase the propensity of hnRNPA2 protein to fibrillize. Rather, the frameshift variants have reduced affinity for the nuclear import receptor karyopherin β2, resulting in cytoplasmic accumulation of hnRNPA2 protein in cells and in animal models that recapitulate the human pathology. Thus, we expand the phenotypes associated with HNRNPA2B1 to include an early-onset form of OPMD caused by frameshift variants that alter its nucleocytoplasmic transport dynamics

Interaction of serum albumins with fluorescent ligand 4-azido coumarin: spectroscopic analysis and molecular docking studies

Steady state fluorescence and time resolved fluorescence studies at 298 K and low temperature phosphorescence (LTP) studies at 77 K of the interaction of bovine serum albumin (BSA) and human serum albumin (HSA) with ligand 4-azido-2H-chromen-2-one or 4-azidocoumarin (4-AC) have been carried out to visualize the location of the binding site and perturbation of the binding site of the tryptophan (Trp)/tyrosine (Tyr) of the protein(s) by monitoring the emission maxima of Trp residue(s) in proteins. The fluorescence quenching study of Trp estimated that the binding constant for both protein–ligand complexes is in the order of ∼10⁶ with binding site 1. Perturbation in the secondary structures of serum albumins due to binding of 4-AC is also observed from circular dichroism (CD) studies. An energy transfer (ET) study further demonstrated that the non-radiative singlet–singlet ET that takes place from the Trp singlet states of proteins to the singlet state of ligands is greater in the case of BSA. This is supported by the distance and orientation of the donor–acceptor pair obtained from molecular docking studies. The molecular docking studies were also fruitfully exploited to understand the involvement of Trp213 in BSA and Trp214 in HSA in the ET process along with the perturbation of the residues around 5 Å from the ligand 4-AC. Phosphorescence spectra at 77 K of the Trp residues in the free proteins (BSA/HSA) and in the complexes of BSA/HSA have also been utilized to specify the role of Trp residues in ET and the binding process

Interaction of multitryptophan protein with drug: An insight into the binding mechanism and the binding domain by time resolved emission, anisotropy, phosphorescence and docking

The interaction of antibiotic Tetracycline hydrochloride (TC) with Alkaline Phosphatase (AP) from Escherichia coli, an important target enzyme in medicinal chemistry, having tryptophan (Trp) residues at 109, 220 and 268 has been studied using the steady state and time resolved emission of the protein and the enhanced emission of the bound drug. The association constant at 298 K (≈10⁶ [M]⁻¹) and the number of binding site (= 1) were estimated using the quenched Trp emission of AP, the enhanced emission and the anisotropy of the bound drug. The values of ΔH⁰ and ΔS⁰ are indicative of electrostatic and H-bonding interaction. The low temperature phosphorescence of free AP and the protein- drug complex and molecular docking comprehensively prove the specific involvement of partially exposed Trp 220 in the binding process without affecting Trp 109 and Trp 268. The Förster energy transfer (ET) efficiency and the rate constant from the Trp residue to TC = 0.51 and ≈10⁸ s⁻¹ respectively. Arg 199, Glu 219, Trp 220, Lys 223, Ala 231, Arg 232 and Tyr 234 residues are involved in the binding process. The motional restriction of TC imposed by nearby residues is reflected in the observed life time and the rotational correlation time of bound TC

Diabetic Foot Ulcer Identification: A Review

Diabetes is a chronic condition caused by an uncontrolled blood sugar levels in the human body. Its early diagnosis may prevent severe complications such as diabetic foot ulcers (DFUs). A DFU is a critical condition that can lead to the amputation of a diabetic patient’s lower limb. The diagnosis of DFU is very complicated for the medical professional as it often goes through several costly and time-consuming clinical procedures. In the age of data deluge, the application of deep learning, machine learning, and computer vision techniques have provided various solutions for assisting clinicians in making more reliable and faster diagnostic decisions. Therefore, the automatic identification of DFU has recently received more attention from the research community. The wound characteristics and visual perceptions with respect to computer vision and deep learning, especially convolutional neural network (CNN) approaches, have provided potential solutions for DFU diagnosis. These approaches have the potential to be quite helpful in current medical practices. Therefore, a detailed comprehensive study of such existing approaches was required. The article aimed to provide researchers with a detailed current status of automatic DFU identification tasks. Multiple observations have been made from existing works, such as the use of traditional ML and advanced DL techniques being necessary to help clinicians make faster and more reliable diagnostic decisions. In traditional ML approaches, image features provide signification information about DFU wounds and help with accurate identification. However, advanced DL approaches have proven to be more promising than ML approaches. The CNN-based solutions proposed by various authors have dominated the problem domain. An interested researcher will successfully be able identify the overall idea in the DFU identification task, and this article will help them finalize the future research goal