Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification

There are a number of studies about extraction of bottleneck (BN) features from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases and triphone states for improving the performance of text-dependent speaker verification (TD-SV). However, a moderate success has been achieved. A recent study [1] presented a time contrastive learning (TCL) concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL bottleneck (BN) feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition (ASR) systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Exploring the direct relationship between GDP per-capita and financial inclusion

Purpose: This paper predicted the direct relationship between the four indicators of “Financial Inclusion” and “GDP per-capita” of the country. Previous studies presented in this scenario are qualitative in nature. Research methodology: In this paper, “step-wise multiple linear regression” is used to establish the cause-and-effect relationship between the four indicators of “financial inclusion”; “Deposit accounts per 1000 population”; “Number of credit accounts per 1,000 people”; “Bank branches per 100,000 of adult population”, and “ATMs per 100,000 of adult population” and “GDP per capita”. Results: Regression model showed only “Credit accounts per 1,000 people” have a significant relationship with the “GDP per capita”. In this article, secondary data were obtained from the RBI website and the reports of international financial institutes. Limitations: Data on “ATMs” and “Bank branches per 100,000 of the adult population” is not present before 2004, decreasing the depth of analysis. Contribution: There is a cause-and-effect relationship between the country’s “GDP per capita” and the “F.I.” “Credit accounts per 1,000 people” only have a significant relationship with GDP per capita, so the change in the number of credit account will show a change in GDP per capita for Indian economy. Keywords: Financial inclusion (F.I), GDP (Gross Domestic Product) per capita, Deposit accounts, Credit accounts, ATMs (Automated Teller Machines), Bank branche

Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding

In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision. Next, we explore the bottleneck (BN) feature extracted by training deep neural networks with a self-supervised objective, autoregressive predictive coding (APC), for TD-SV and compare it with the well-studied speaker-discriminant BN feature. The proposed VTL method is then applied to APC and speaker-discriminant BN features. In the end, we combine the VTL perturbation systems trained on MFCC and the two BN features in the score domain. Experiments are performed on the RedDots challenge 2016 database of TD-SV using short utterances with Gaussian mixture model-universal background model and i-vector techniques. Results show the proposed methods significantly outperform the baselines.Comment: Copyright (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

EFFECT OF 8 WEEKS HIGH INTENSITY INTERVAL TRAINING ON MAXIMUM OXYGEN UPTAKE CAPACITY AND RELATED CARDIO-RESPIRATORY PARAMETERS AT ANAEROBIC THRESHOLD LEVEL OF INDIAN MALE FIELD HOCKEY PLAYERS

Aim: In order to achieve maximal performance, need for high oxygen uptake is complemented with rigorous training program. To evaluate the effect of 8 weeks high intensity interval training (HIIT) on maximum oxygen uptake capacity and related cardio-respiratory parameters at anaerobic threshold level. Materials and methods: High intensity interval training programme was implemented among 20 trained young male hockey players for 3 days/week. The training set included 2 minutes of intense sprint workout followed by a minute each of active recovery and complete rest. The point of anaerobic threshold was identified with ventilatory equivalent method while the players were subjected to exercise on computerized bicycle ergometer. Results: Present study depicts significant increase in maximum oxygen consumption (+8%, p=0.000), maximum heart rate (+3%, p=0.01) and glycogen content (+3%, p=0.421) with significant decrease in pre-exercise heart rate (-7%, p=0.001), recovery heart rate (-7%, p=0.000) and average breathing frequency (-6%, p=0.014) after 8 weeks of interval training. Oxygen consumption (p=0.505), heart rate (p=0.000) and work load (p=0.004) were also improved significantly at anaerobic threshold level by 11%, 6% and 9% respectively. Conclusion: HIIT protocol ultimately allows the athlete to exercise at higher workload with greater cardiac proficiency within the aerobic zone.  Article visualizations

Etiological study of generalized lymphadenopathy in a tertiary care hospital

Background: This study was done to know about the clinical biochemical as well as radiological profile of patients presented as generalized lymphadenopathy in a tertiary care centre and to know the different causes of generalized lymphadenopathy.Methods: 116 patients of generalized lymphadenopathy were included this study based on the inclusion and exclusion criteria. Detailed history, physical examination and relevant systemic examination including detailed examination of lympho-reticular system were done as per a structured proforma and necessary lab investigations were done for confirming diagnosis.Results: Among 116 patients of generalized lymphadenopathy 59.5% were non-malignant causes where 40.5% diagnosed as malignant causes. Among them tuberculosis consist of 39 (33.6%), NHL 18 (15.5%), reactive lymphadenopathy 16 (13.8%), CLL and HD 8 (6.9%) each, ALL 7 (6%), SLE 5(4.3%), Kikuchi’s disease 4 (3.4%), AML and RA 3 (2.6%) each and castleman’s disease, phenytoin lymphadenopathy, metastatic lung and breast carcinoma 1 (0.9%) each. Cervical groups of lymph nodes were most commonly involved 86 patients (74.1%) followed by axillary groups 73 patients (62.9%). Lymph nodes size 1.5cm were due to malignant and non-malignant granulomatous cases. FNAC give definite diagnosis 80.9% malignant cases where 76.8% in non-malignant cases. HPE shown definite diagnosis in 100% cases both malignant and non-malignant diseases.Conclusions: Tuberculosis is most common cause of generalized lymphadenopathy followed by lymphoma. And reactive lymphadenitis is also an important consideration.

Basal-bolus insulin therapy during switching over from continuous intra venous regular insulin to sub cutaneous insulin therapy as compared to conventional regimens in type-2 diabetes patients admitted

Background: According to the World Health Organization (WHO), over 347 million people worldwide have diabetes. Latest estimates reveal that 25.4 million Americans have diabetes mellitus (DM), with up to 95% of those having type 2 DM. This study was done to know the effects of Glargine insulin plus human regular insulin on blood sugar control while switching over from continuous IV insulin infusion to SC route as compared to conventional SC insulin regimens in CCU setup.Methods: 65 patients of T2DM were included this study. Detailed history, physical examination and relevant systemic examination were performed and necessary lab investigations were done.Results: The mean age was 49.52±10.16. Mean FPG on 1st Day: The p value of B-B against PRE is significant. Mean FPG on Day of discharge: The p value of B-B against PRE is significant and B-B against NPH is also significant. Mean FPG 2Weeks after discharge: The p value of B-B against PRE is significant and B-B against NPH is also significant. Mean PPPG on 1st Day: The p value of B-B against NPH is significant. Mean PPPG on day of discharge: The p value of B-B against PRE is significant. Mean PPPG 2weeks after discharge: The p value of B-B against PRE is significant. Hypoglycemia was occurring in 25, 15, and 25 in BB, NPH, and PRE group respectively. The p value is significant when NPH compared to PRE.Conclusions: B-B regimen was better than other regimen for controlling FPG and PPPG. The insulin dose was high in NPH regimen compared to both B-B and PRE regimens. 

A study of stroke patients with respect to their clinical and demographic profile and outcome

Background: The incidence of cerebrovascular diseases increases with age and the number of strokes is projected to increase as the elderly population grows, with an effect of doubling in stroke deaths in the United States by 2030. This study was done to know the clinical demographic profiles and outcome of the patients presented with stroke in a tertiary care centre.Methods: 501 patients of stroke were included this study. Detailed history, physical examination and relevant systemic examination including detailed examination of neurological system were performed and necessary lab investigations were done.Results: Among 501 stroke patients 90 (18%) patients were of young and 236 (47.1%) of elderly (>60years). Among them 435 (86.8%) were hypertensive and 130 (25.9%) had H/O diabetes and 160(75.83%) had dyslipidemia. In CT scan or MRI of brain, 125 (25%) had lacunar infarction, 76 (15.1%) had non-lacunar infarction, 180 (35.9%) had parenchymal hemorrhage with no ventricular extension and 120 (24%) had parenchymal hemorrhage with ventricular extension. All patients who expired (n=95) presented with poor GCS (≤8) on admission regardless of the stroke subtypes. Among all lacunar infarctions, 92% occurred in hypertensive individuals and among all hemorrhagic strokes, 93.33% occurred in hypertensive patients. Non-lacunar infarction is the most common type of stroke among non-hypertensives (54.55%). And infarction is the most common type of stroke events in diabetics.Conclusions: Stroke can occur at any age, but the elderly persons are more commonly affected with a slight predilection to male. The hemorrhagic stroke outnumbers the ischemic stroke mainly because of uncontrolled hypertension. The GCS at presentation can predict the stroke outcome. Risk factors of stroke include Hypertension, smoking, high cholesterol and Diabetes, obesity, lack of exercise, and genetic factors