Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech

Neural Networks : the Official Journal of the International Neural Network Society
Swapna Agarwalla, Kandarpa Kumar Sarma

Abstract

Automatic Speaker Recognition (ASR) and related issues are continuously evolving as inseparable elements of Human Computer Interaction (HCI). With assimilation of emerging concepts like big data and Internet of Things (IoT) as extended elements of HCI, ASR techniques are found to be passing through a paradigm shift. Oflate, learning based techniques have started to receive greater attention from research communities related to ASR owing to the fact that former possess natural ability to mimic biological behavior and that way aids ASR modeling and processing. The current learning based ASR techniques are found to be evolving further with incorporation of big data, IoT like concepts. Here, in this paper, we report certain approaches based on machine learning (ML) used for extraction of relevant samples from big data space and apply them for ASR using certain soft computing techniques for Assamese speech with dialectal variations. A class of ML techniques comprising of the basic Artificial Neural Network (ANN) in feedforward (FF) and Deep Neural Network (DNN) forms using raw speech, extracted features and frequency domain forms are considered. The Multi Layer Perceptron (MLP) is configured with inputs in several forms to learn cla...Continue Reading

References

Jan 1, 1995·IEEE Transactions on Neural Networks·U A MullerW Guggenbuhl

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Citations

Sep 19, 2019·Sensors·Jingmei LiPeng Gao
Mar 9, 2021·Journal of Cardiovascular Pharmacology and Therapeutics·Steven T SimonMichael A Rosenberg
Jul 8, 2020··Qidong LuYun Xie

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