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International Journal of Speech Technology
Erschienen in: International Journal of Speech Technology 1/2022

16.10.2020

Hindi speech recognition using time delay neural network acoustic modeling with i-vector adaptation

verfasst von: Ankit Kumar, Rajesh Kumar Aggarwal

Erschienen in: International Journal of Speech Technology | Ausgabe 1/2022

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Abstract

It is a need of time to build an Automatic Speech Recognition (ASR) system for low and limited resource languages. Usually, statistical techniques such as Hidden Markov Models (HMM) have been applied for Indian language ASR systems for the last two decades. In this work, we have selected the Time-delay Neural Network (TDNN) based acoustic modeling with i-vector adaptation for limited resource Hindi ASR. The TDNN can capture the extended temporal context of acoustic events. To reduce the training time, we used sub-sampling based TDNN architecture in this work. Further, data augmentation techniques have been applied to extend the size of training data developed by TIFR, Mumbai. The results show that data augmentation significantly improves the performance of the Hindi ASR. Further, \(\approx\) 4% average improvement has been recorded by applying i-vector adaptation in this work. We found the best system accuracy of 89.9% with TDNN based acoustic modeling with i-vector adaptation.
Vorheriger Artikel Normalized approximate descent used for spike based automatic bird species recognition system
Nächster Artikel Boosting subjective quality of Arabic text-to-speech (TTS) using end-to-end deep architecture

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Metadaten
Titel
Hindi speech recognition using time delay neural network acoustic modeling with i-vector adaptation
verfasst von
Ankit Kumar
Rajesh Kumar Aggarwal
Publikationsdatum
16.10.2020
Verlag
Springer US
Erschienen in
International Journal of Speech Technology / Ausgabe 1/2022
Print ISSN: 1381-2416
Elektronische ISSN: 1572-8110
DOI
https://doi.org/10.1007/s10772-020-09757-0

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