The performance of speaker identification systems has improved due to recent advances in speech processing techniques but there is still need of improvement in term of text-independent speaker identification and suitable modelling techniques for voice feature vectors. It becomes difficult for person to recognize a voice when an uncontrollable noise adds in to it. In this paper, feature vectors from speech are extracted by using mel-frequency cepstral coefficients and vector quantization technique is implemented through Linde-Buzo-Gray algorithm. Two purposeful speech databases with added noise, recorded at sampling frequencies 8000 Hz and 11025 Hz, are used to check the accuracy of the developed speaker identification system in non-ideal conditions. An analysis is also provided by performing different experiments on the databases that number of vectors in VQ codebook and sampling frequency influence the identification accuracy significantly.

In an era where technology shapes education, effectively engaging students remains a critical challenge. Student engagement significantly impacts academic performance, yet traditional assessment methods fail to capture its multidimensional nature. This study proposes a novel Engagement Level Classification Framework (ELCF) that employs deep learning models to classify engagement into five distinct levels (H1, H2, M, L2, L1) based on behavioral indicators, emotional cues, and academic performance. A multi-modal dataset (N entries), integrating facial expressions, student behaviors, and cognitive performance, enables a holistic engagement assessment. The system further employs real-time AI-driven analysis to personalize course recommendations, adapting dynamically to student engagement states. The proposed framework was evaluated using deep learning architectures such as EfficientNetB0, CNN, and ensemble models, achieving up to 94% accuracy in engagement classification. The adaptive recommendation system attained an F1-score of 84% and a 92% hit rate, demonstrating its effectiveness in aligning content with student engagement levels. However, certain limitations remain, including potential biases in facial emotion recognition, privacy concerns in real-time monitoring, and dataset scalability constraints. Future research will focus on bias mitigation, expanding dataset diversity, and refining adaptive interventions to enhance system reliability and inclusivity. These findings contribute to the academic discourse on AI-driven student engagement monitoring, offering quantifiable evidence for the development of fair, transparent, and ethically responsible technology-enhanced learning environments.

It is often uncomfortable for disabled individuals, especially those with vision impairment, to conduct educational activities in collaboration with people that have perfect vision. This can be because of the former’s lack of confidence, vision capability, and acceptance. Information and communications technology (ICT) has played a vital role in giving support to people with visual impairments so that they can overcome their issues. This study proposes innovative solutions that address the challenges faced by partially or completely visually impaired people. It provides an interactive and intelligent interface, which they may use to perform educational activities, such as editing, writing, or reviewing documents, in collaboration with people without visual impairments. The system provides high-quality awareness features by sending them instant voice notifications about the actions and events occurring in the shared environment. A speech-recognition engine has been integrated into the system to allow users to interact with the application through voice commands. The system is evaluated through experiments, where people with visual impairment and people without visual impairment were engaged in collaborative writing. The obtained results are encouraging. The users showed curiosity in the system and were able to focus on the productive task instead of their disability.