Multidimensional feature diversity based speech signal acquisition
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Multidimensional feature diversity based speech signal acquisition Konduru Ashok Kumar1 · J. L. Mazher Iqbal2 Received: 30 March 2020 / Accepted: 8 July 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Computer intelligence methods are the buzz of identifying the state and context of diversified domains. The computer intelligence methods mostly fall into the domain of machine learning, which often trained by the known data of the domain context and state. These methods even spanned significantly to the diversified contexts of speech domains such as speech recognition, emotion recognition, speaker recognition, and many more. However, the contemporary methods of speech acquisition or speech separation have majorly relied on filters and other signal processing techniques, which are considerably underrated to identify the speech signal that exists among the highly correlated noise signals. In this context, the contribution of this manuscript is a machine learning mechanism that intended to identify the speech signal bounded with highly correlated noise signals. The method proposed is using the diversified features extracted from the signals representing the speech and other correlated noises. The experimental study has carried on the signal and correlated noise utterances collected from the benchmark dataset CHiME-5, which has meant for machine learning-based signal processing. The performance analysis of the proposed model has scaled by comparing it with the contemporary model. Keywords PCM (pulse-code modulation) · Wiener multi-channel filter method · Deep neural networks · WER (word-errorrate)
1 Introduction The term speech signal processing has signified as processing the speech primarily and then it has processed as digital processing. It includes signals such as image, electrocardiogram, control system, and audio signals. The integration of signal processing and speech processing has termed to be speech signal processing. The term noise has used for unimportant modifications, where the signal might suffer at the time of storage, processing, conversion, transmission, and capturing in the signal processing stream.Both digital and analog signals have been interfered with by signal noise. Nevertheless, the quantity of noise needed for impacting the signal is maximum. * Konduru Ashok Kumar [email protected] J. L. Mazher Iqbal [email protected] 1
Dept., of ECE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
Dept., of ECE, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India
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The work (Bar-Am 2014) presents that as per Kuhn’s scientific revolutions concept, the progress is made by science through revolutionary variations of prevalent scientific models, where the model depicts the set of values, beliefs, also methodological and technical processes common in the scientific community. Here, paradigms or models determine frames to solve scientific tasks. Novel solutions come throug
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