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The encouraging trend of usage of human machine interfaces in diverse areas has driven the evolution of Automatic Speech Recognition (ASR) systems during last two decades. Lately, the inclination has been towards the use of machine learning techniques for under-resourced human languages primarily to focus on designing of voice activated digital tool for a sizable portion of computer illiterate speakers. A vast majority of the works in this field have employed shallow models like conventional Artificial Neural Network and Hidden Markov Model in combination with Mel Frequency Cepstral Coefficients and other relevant features for the applications of speech recognition systems. Although these shallow models are found effective, but to minimize human intervention from the approach and also to yield the better system performance, recent research has focused to incorporate deep learning models for ASR applications especially for under-resourced languages. Sylheti language, a member of Indo-Aryan language group, is an under resourced language which has more than 10 million Sylheti speakers living across the world mostly in India and Bangladesh. Focusing on the need of an ASR model for Sylheti, this work aims to design a robust ASR model for an under resourced language Sylheti by employing state-of-the-art deep learning technique Convolutional Neural Network (CNN). To find out the best and suitable ASR model for Sylheti, certain ASR approaches are formulated and trained by Sylheti isolated and connected words. The specially configured ASR model based on CNN is trained with clean, and noisy speech data which are necessary for training and making the system robust. Thereafter, a comparative analysis is presented by configuring the ASR model by some shallow models like Feed-forward neural network, Recurrent neural Network, Hidden Markov model and Time Delay neural Network. Experimental results indicate that the proposed CNN based ASR system works well for Sylheti language and the performance accuracy obtained by the system is found to be satisfactory despite the system demonstrating certain training latency. [ABSTRACT FROM AUTHOR]

The defect classification of knitted fabrics is a challenging area of research. Most of the defect detection works in India; Bangladesh is being carried by manually trained inspectors. The long working hours and the working environment at the company induces the fatigue, lack of concentration, and triteness to the workers due to this they may not able to detect the defects on the clothes after it is manufactured. To overcome this problem, a computer-aided defect detection system is being developed using digital image processing and artificial neural Network methods. The two types of artificial neural networks were applied to compare the results obtained. The networks were: a back propagation based feed forward neural network and the other was Levenberg–Marquardt (LM) algorithm based back propagation network. Experimental results predicted detection of a high degree of variety of fabric defects. [ABSTRACT FROM AUTHOR]

Investigations of changing the flow regime and flow prediction are of vital societal and hydro-ecological importance in a transboundary river like Punarbhaba river between India and Bangladesh. The present paper investigated flow regime though advance periodicity models (Morlet's wavelet transformation) at the seasonal scale. Flow prediction using advanced machine learning techniques like Support vector machine (SVM), Artificial Neural Network (ANN), Hybrid wavelet ANN (W-ANN), Random forest(RF) capturing the periodicity, duration, cyclic or semi-cyclic nature of flow wave or wavelet from the historical time series data (1978–2017) is very crucial for environmental flow management and estimating present and future states of environmental flow. Flow alteration modeling (using heat map) and estimation of environmental flow (using duration curve shifting and RVA methods) are another vital objectives of this work to know the present and future hydro-ecological state. The result of periodicity clearly identified two distinct flow regimes before and after 1992-93 triggered by damming over there in 1992. All the prediction models identified the declining trend of flow in all the seasons, however hybrid wavelet ANN model could be treated as the best suited because of its very high-performance level. The degree of hydrological alteration is identified very high in the post-hydrological alteration (PHA) (post-1992) period and it is likely to be intensified predicted period. The estimated environmental flow state in PHA falls under moderate to critically modified states but if alteration goes on in this way ecological deficit will be the obvious result. For the survival of the river estimated environmental flow could be released primarily. Image 1 • The periodicity analyses for annual and seasonal flow were performed using wavelet. • The flow has been forecasted for 13 years using machine-learning methods. • The hydrological alteration has been estimated. • The present and future environmental flow has been estimated. [ABSTRACT FROM AUTHOR]