ITM Web Conf.
Volume 54, 20232nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
|Number of page(s)||8|
|Published online||04 July 2023|
Enhancing Performance of End-to-End Gujarati Language ASR using combination of Integrated Feature Extraction and Improved Spell Corrector Algorithm
Department of Computer Engineering, National Institute of Technology, Kurukshetra, India
A number of intricate deep learning architectures for effective End-to-End (E2E) speech recognition systems have emerged due to recent advancements in algorithms and technical resources. The proposed work develops an ASR system for the publicly accessible dataset on Gujarati language. The approach provided in this research combines features like Mel frequency Cepstral Coefficients (MFCC) and Constant Q Cepstral Coefficients (CQCC) at front-end feature extraction methodologies. Enhanced spell corrector with BERT-based algorithm and Gated Recurrent Units (GRU) based DeepSpeech2 architecture are used to implement the back end portion of the proposed ASR system. The proposed study shown that combining the MFCC features and CQCC features extracted from speech with the GRU-based DeepSpeech2 model and the upgraded or enhanced spell corrector improves the Word Error Rate (WER) by 17.46% when compared to the model without post processing.
Key words: ASR / MFCC / CQCC / Spell Corrector / Bidirectional Encoder Representations from Transformers (BERT) / DeepSpeech2 model
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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