{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:19:52Z","timestamp":1777706392357,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,8,10]]},"abstract":"<jats:p>Speech recognition has now become ubiquitous and plays an inevitable role in almost all sectors. Numerous works have been proposed on speech recognition; however, more accurate transcriptions are not possible. Exploration of various studies related to spell correction implies that several kinds of research have been carried out in this field but still it is a very challenging problem. This led to the need for a new spell corrector framework capable of leveraging the performance of the automatic speech recognition (ASR) system. The proposed work unveils state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) based spell correction module developed on top of the deep recurrent neural network (RNN) based ASR system. The impact of BERT-based spell correction on the ASR system is evaluated on three different accent datasets in the perspective of word error rate (WER), character error rate (CER), and Bilingual evaluation understudy (BLEU) score. The experimental results inferred that the enhanced spell correction module is efficacious in detecting and correcting spell errors, by achieving the WER of 5.025% on librispeech corpus, 6.35% on voxforge, and 7.05% on NPTEL corpus.<\/jats:p>","DOI":"10.3233\/jifs-213332","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T11:50:31Z","timestamp":1653393031000},"page":"4873-4882","source":"Crossref","is-referenced-by-count":8,"title":["Towards improving speech recognition model with post-processing spell correction using BERT"],"prefix":"10.1177","volume":"43","author":[{"given":"M.C.","family":"Shunmuga Priya","sequence":"first","affiliation":[{"name":"Department of IT, PSG College of Technology, Coimbatore, India"}]},{"given":"D.","family":"Karthika Renuka","sequence":"additional","affiliation":[{"name":"Department of IT, PSG College of Technology, Coimbatore, India"}]},{"given":"L.","family":"Ashok Kumar","sequence":"additional","affiliation":[{"name":"Department of EEE, PSG College of Technology, Coimbatore, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-213332_ref2","doi-asserted-by":"crossref","unstructured":"Arthur Neto A. , Toselli A.H. and Byron Bezerra , Towards the natural language processing as spelling correction for offline handwritten text recognition systems, Applied Sciences 10 (2020).","DOI":"10.3390\/app10217711"},{"issue":"7","key":"10.3233\/JIFS-213332_ref7","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.3390\/electronics9071157","article-title":"End-to-End Noisy Speech Recognition Using Fourier and Hilbert Spectrum 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Bezerra and Alejandro H\u00b4ctor Toselli , Towards the natural language processing as spelling correction for offline handwritten text recognition systems, Applied Sciences 10 (2020), no. 21.","DOI":"10.3390\/app10217711"},{"key":"10.3233\/JIFS-213332_ref17","doi-asserted-by":"crossref","unstructured":"Rushab Munot and Ani Nenkova , Emotion Impacts Speech Recognition Performance, In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 16\u201321, Minneapolis, Minnesota, Association for Computational Linguistics, 2019.","DOI":"10.18653\/v1\/N19-3003"},{"key":"10.3233\/JIFS-213332_ref18","doi-asserted-by":"crossref","unstructured":"Stephane Clinchant , Kweon Woo Jung and Vassilina Nikoulina , On the use of BERT for Neural Machine Translation, In Proceedings of the 3rd Workshop on Neural Generation and Translation, pages 108\u2013117, Hong Kong, Association for Computational Linguistics, 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