Abstract
Automatic speech recognition has allowed human beings to use their voices to speak with a computer interface. Nepali speech recognition involves conversion of Nepali language to corresponding text in Devanagari lipi. This work proposes a novel approach for developing Nepali Speech recognition model based using CNN-GRU. The data is collected from the Librispeech. The collected data is pre-processed and MFCC is applied on it for feature extraction. CNN-GRU model is responsible for extraction of the features and development of the acoustic model. CTC is responsible for decoding. The performance of the developed model has been assessed using Word Error Rate of the transcribed text.
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Acknowledgement
This work has been supported by the University Grants Commission, Nepal under a Faculty Research Grant (UGC Award No. FRG-76/77-Engg-1) for the research project “Preparation of Nepali Speech Corpus: Step towards Efficient Nepali Speech Processing”.
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Joshi, B., Bhatta, B., Panday, S.P., Maharjan, R.K. (2022). A Novel Deep Learning Based Nepali Speech Recognition. In: Mekhilef, S., Shaw, R.N., Siano, P. (eds) Innovations in Electrical and Electronic Engineering. ICEEE 2022. Lecture Notes in Electrical Engineering, vol 894. Springer, Singapore. https://doi.org/10.1007/978-981-19-1677-9_39
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DOI: https://doi.org/10.1007/978-981-19-1677-9_39
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