Robust Perceptual Wavelet Packet Features for Recognition of Continuous Kannada Speech

An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 80% of collected speech data is used for training the acoustic m...

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Bibliographic Details
Published in:Wireless personal communications 2021-12, Vol.121 (3), p.1781-1804
Main Authors: Mahadevaswamy, Ravi, D. J.
Format: Article
Language:English
Subjects:
Communications Engineering
Computer Communication Networks
Engineering
Feature extraction
Feature recognition
Filter banks
Networks
Signal,Image and Speech Processing
Speech
Speech recognition
Words (language)
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Summary:An ASR system is built for the Continuous Kannada Speech Recognition. The acoustic and language models are created with the help of the Kaldi toolkit. The speech database is created with the native male and female Kannada speakers. The 80% of collected speech data is used for training the acoustic models and 20% of speech database is used for the system testing. The Performance of the system is presented interms of Word Error Rate (WER). Wavelet Packet Decomposition along with Mel filter bank is used to achieve feature extraction. The proposed feature extraction performs slightly better than the conventional features such as MFCC, PLP interms of WRA and WER under uncontrolled conditions. For the speech corpus collected in Kannada Language, the proposed features shows an improvement in Word Recognition Accuracy (WRA) of 1.79% over baseline features.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-021-08736-1