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A Revisit to Speech Processing and Analysis

by Aniruddha Mohanty, Ravindranath C. Cherukuri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 30
Year of Publication: 2020
Authors: Aniruddha Mohanty, Ravindranath C. Cherukuri
10.5120/ijca2020920840

Aniruddha Mohanty, Ravindranath C. Cherukuri . A Revisit to Speech Processing and Analysis. International Journal of Computer Applications. 175, 30 ( Nov 2020), 1-6. DOI=10.5120/ijca2020920840

@article{ 10.5120/ijca2020920840,
author = { Aniruddha Mohanty, Ravindranath C. Cherukuri },
title = { A Revisit to Speech Processing and Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2020 },
volume = { 175 },
number = { 30 },
month = { Nov },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number30/31638-2020920840/ },
doi = { 10.5120/ijca2020920840 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:39:50.324296+05:30
%A Aniruddha Mohanty
%A Ravindranath C. Cherukuri
%T A Revisit to Speech Processing and Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 30
%P 1-6
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Speech recognition is an active area in signal processing.Various researchers have been invested different concepts in speech recognition system as part of feature extraction techniques, speech classifiers, statistical analysis, encompassing mathematical models, signal processing and transformations, database and performance evaluation. In the current era, multi speaker analysis is the newly focused area in speech processing and analysis. It includes audio segmentation, extraction of relevant features, classification of features, template generation and training. Also, other techniques like Bank-of-filters, Linear Predictive Coding Model, Vector Quantization, Hidden Markov Model and Gaussian Mixture Model to get better result. In this paper, various approaches have been analyzed based on acoustic and articular features focusing on Human Auditory System (HAS). Even focusing on the cross functional approach by using machine learning, artificial intelligence-based techniques and neural networks.

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Index Terms

Computer Science
Information Sciences

Keywords

Automatic speech recognition feature extraction dimension reduction modeling and matching techniques

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