Image-based features for speech signal classification

Like other applications, under the purview of pattern classification, analyzing speech signals is crucial. People often mix different languages while talking which makes this task complicated. This happens mostly in India, since different languages are used from one state to another. Among many, Sou...

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Bibliographic Details
Published in:Multimedia tools and applications 2020-12, Vol.79 (47-48), p.34913-34929
Main Authors: Mukherjee, Himadri, Dhar, Ankita, Obaidullah, Sk Md, Phadikar, Santanu, Roy, Kaushik
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Classification
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Experiments
Feature extraction
Identification
Image classification
Language
Languages
Multimedia
Multimedia Information Systems
Neural networks
Pattern analysis
Signal classification
Special Purpose and Application-Based Systems
Speech
Voice recognition
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Summary:Like other applications, under the purview of pattern classification, analyzing speech signals is crucial. People often mix different languages while talking which makes this task complicated. This happens mostly in India, since different languages are used from one state to another. Among many, Southern part of India suffers a lot from this situation, where distinguishing their languages is important. In this paper, we propose image-based features for speech signal classification because it is possible to identify different patterns by visualizing their speech patterns. Modified Mel frequency cepstral coefficient (MFCC) features namely MFCC- Statistics Grade (MFCC-SG) were extracted which were visualized by plotting techniques and thereafter fed to a convolutional neural network. In this study, we used the top 4 languages namely Telugu, Tamil, Malayalam, and Kannada. Experiments were performed on more than 900 hours of data collected from YouTube leading to over 150000 images and the highest accuracy of 94.51% was obtained.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08553-6