Speech is the most prominent and natural form of communication between humans. Human beings have long been motivated to create computer that can understand and talk like human. When the research tries to develop certain recognition system they require certain previously stored data i.e. database for respective recognition system. There are various speech databases available for European Language but very less for Indian Language. In this paper we discuss the various Speech Database developed in different Indian Languages for speech recognition system & Text to Speech System.
The research work describes the procedure and the development of Isolated Marathi Emotional Speech Database. The database consists of samples, collected from 50 speakers including males and females who simulated the emotions producing by the Marathi utterances which are used in everyday communication and are interpretable in all applied emotions. The speech samples were enhanced by spectral subtraction method and distinguished by the various real life situations. The recorded speech samples were categorized in three basic categories i.e. Happy, Sad and Angry.
Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease's detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from "ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101" show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease's detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.
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