Improving Indian Spoken-Language Identification by Feature Selection in Duration Mismatch Framework

Paper presents novel duration normalized feature selection technique and two-step modified hierarchical classifier to improve the accuracy of spoken language identification (SLID) using Indian languages for duration mismatched condition. Feature selection averages random forest-based importance vect...

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Bibliographic Details
Published in:SN computer science 2021-11, Vol.2 (6), p.442, Article 442
Main Authors: Bakshi, Aarti, Kopparapu, Sunil Kumar
Format: Article
Language:English
Subjects:
Accuracy
Acoustics
Algorithms
Classifiers
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Datasets
Discriminant analysis
Feature selection
Identification
Information Systems and Communication Service
Language
Languages
Neural networks
Original Research
Pattern Recognition and Graphics
Software Engineering/Programming and Operating Systems
Speech
Support vector machines
Vision
Wavelet transforms
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Summary:Paper presents novel duration normalized feature selection technique and two-step modified hierarchical classifier to improve the accuracy of spoken language identification (SLID) using Indian languages for duration mismatched condition. Feature selection averages random forest-based importance vectors of open SMILE features of different duration utterances. Although it improves the SLID system’s accuracy for mismatched training and testing durations, the performance is significantly reduced for short-duration utterances. A cascade of inter-family and intra-family classifiers with an additional class to improve false language family estimation. All India Radio data set with nine Indian languages and different utterance durations was used as speech material. Experimental results showed that 150 optimal features with the proposed modified hierarchical classifier showed the highest accuracy of 96.9 % and 84.4 % for 30 s and 0.2 s utterances for the same train-test duration. However, we achieved an accuracy of 98.3 % and 61.9 % for 15 and 0.2 s test duration when trained with 30 s duration utterance. Comparative analysis showed a significant improvement in accuracy than several SLID systems in the literature.
ISSN:2662-995X
2661-8907
DOI:10.1007/s42979-021-00750-1