ITM Web Conf.
Volume 32, 2020International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|Number of page(s)||6|
|Published online||29 July 2020|
Indian Language Identification using Deep Learning
Department of Electronics Engineering, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
Spoken language is the most regular method of correspondence in this day and age. Endeavours to create language recognizable proof frameworks for Indian dialects have been very restricted because of the issue of speaker accessibility and language readability. However, the necessity of SLID is expanding for common and safeguard applications day by day. Feature extraction is a basic and important procedure performed in LID. A sound example is changed over into a spectrogram visual portrayal which describes a range of frequencies in regard with time. Three such spectrogram visuals were generated namely Log Spectrogram, Gammatonegram and IIR-CQT Spectrogram for audio samples from the standardized IIIT-H Indic Speech Database. These visual representations depict language specific details and the nature of each language. These spectrograms images were then used as an input to the CNN. Classification accuracy of 98.86% was obtained using the proposed methodology.
Key words: Convolutional Neural Network (CNN) / Spoken Indian Language Identification (SLID) / Log Spectrogram, gammatonegram / IIR-CQT Spectrogram / Artificial Neural Network (ANN) / Deep Learning
© The Authors, published by EDP Sciences, 2020
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