A GMM supervector approach for spoken Indian language identification for mismatch utterance length

Volume: 10, Issue: 2, Pages: 1114 - 1121
Published: Apr 1, 2021
Abstract
Gaussian mixture model-universal background model (GMM UBM) supervectors are used to identify spoken Indian languages. The supervectors are calculated from short-time MFCC, its first and sec derivatives. The UBM builds a generalized Indian language model, and mean adaptation transforms it to a duration normalized language-specific GMM. Multi-class support vector machine and artificial neural network classifiers are used to identify language...
Paper Details
Title
A GMM supervector approach for spoken Indian language identification for mismatch utterance length
Published Date
Apr 1, 2021
Volume
10
Issue
2
Pages
1114 - 1121
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