Education System in Bangladesh Amid COVID-19: Traditional Scenario, Emergency Protocols, Challenges and a Proposed Sustainable Conceptual Framework

This study demonstrates how significantly the COVID-19 pandemic has affected Bangladesh's formal education system. Despite the fact that Bangladesh introduced Information and Communication Technology (ICT) in educational practices before the pandemic, effective ICT deployment could not be integrated at all levels. Even though online classes and other print- and electronic media-based approaches evolved into the "new normal" in an effort to address the difficulties brought on by the pandemic, both teachers and students have faced and continue to encounter many challenges. A convergent parallel mixed method design was followed for this study. Data were collected from 205 Bangladeshi students and 50 Bangladeshi teachers through semi-structured questionnaires. In addition, 11 parent interviews and 12 Key Informant Interviews were conducted. According to the findings, the lack of proper training for teachers, poor socio-economic conditions, lack of internet availability and speed, the shortage of ICT equipment, students not being technologically exposed, and the disruption of electricity are major issues hindering the fruitful implementation of online education. Creating an ICT framework, providing subsidised internet for students and instructors for instructive purposes, promoting alternative means to carry on formal education, dedicating instruction hours in TV and radio channels, ensuring proper IT infrastructure and tools, and taking initiatives to promote the learning management system can play a significant role in creating the ideal environment to promote online education. In summary, this study suggests a holistic framework to continue formal teaching-learning in different levels of education to achieve sustainable development goals (SDG) without any disruption in emergency contexts such as the COVID-19 pandemic.

Spoken Language Identification of Indian Languages in Adversarial Synthetic and Noisy Attacking Environments

During the COVID19 epidemic, the need for contact-less biometric ASV systems is at an all-time high. As a result, voice technology based ASV systems are in more demand. In current age of Artificial Intelligence, a variety of spoofing attacks poses a significant front end threat to these systems. Such units are also constantly exposed to noise settings. In these adversarial environments, a spoken language identification module in multi-lingual systems should provide better performance. As a result, the goal of this research is to resolve the ambiguity in identifying spoken language in noisy and synthetic voice spoofing attacks. In this paper, fused CQCC-MFCC feature set is combined with a Convolutional Neural Network(CNN) for increasing performance of language detection in artificial voice attack in a noisy environment. Nine Indian languages considered here are Bengali,Gujarati,Hindi,Malayalam, Manipuri,Odia,Rajasthani,Tamil and Telugu. It was found to have a 97 percent accuracy on the INDIC TTS Database. The use of CQCC in tandem with MFCC improves accuracy by 1 % as compared to using only MFCC features. © 2022 IEEE.