A spoken query system to access the real time agricultural commodity prices and weather information in Kannada language/dialects

We develop two improvements over our previously proposed spectral subtraction with voice activity detection and minimum mean square error spectrum power estimator based on zero crossing (SS-VAD + MMSE-SPZC) enhancement for a real-time spoken query system (SQS). Firstly, we introduce a time delay neu...

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Bibliographic Details
Published in:Multimedia tools and applications 2024-03, Vol.83 (10), p.28675-28688
Main Authors: G, Thimmaraja Yadava, G, Nagaraja B, S, Jayanna H, R, Shivakumar B
Format: Article
Language:English
Subjects:
Accuracy
Adaptation
Agricultural commodities
Agricultural production
Automatic speech recognition
Background noise
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Dialects
Kannada
Language
Meteorological data
Multimedia
Multimedia Information Systems
Neural networks
Prices
Real time
Special Purpose and Application-Based Systems
Speech
Speech recognition
Time
Time lag
Voice activity detectors
Voice recognition
Weather
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Summary:We develop two improvements over our previously proposed spectral subtraction with voice activity detection and minimum mean square error spectrum power estimator based on zero crossing (SS-VAD + MMSE-SPZC) enhancement for a real-time spoken query system (SQS). Firstly, we introduce a time delay neural network (TDNN) based modeling technique. Secondly, to properly train the models, we increase the size of the database by collecting the Kannada speech data from an additional 500 farmers under real-time conditions. The proposed combined enhancement technique effectively removes background noise and improves speech quality. When evaluated on the updated degraded speech corpus, our proposed automatic speech recognition (ASR) system achieves better performance compared to previous framework. Moreover, experimental results demonstrate an improvement of 1.32% and 1.48% in terms of speech recognition accuracy for noisy and enhanced speech data respectively, compared to our earlier work.
ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-023-16554-9