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Central Kurdish Automatic Speech Recognition using Deep Learning

Central Kurdish Automatic Speech Recognition using Deep Learning

Authors

Abdulhady Abas Abdullah , Hadi Veisi

Journal of University of Anbar for Pure Science

Vol. 16

pp. 108-118

January 1, 2022

Part of Automatic Speech Recognition (ASR) Project

Abstract

Develops a Central Kurdish automatic speech recognition system using deep learning and transfer learning. The approach combines MFCC speech features with LSTM networks and Connectionist Temporal Classification and uses the AsoSoft speech database.

Keywords

Automatic Speech Recognition Central Kurdish Sorani LSTM CTC Deep Learning

Related Datasets

AsoSoft Speech Corpus

2021 43.68 hours (full corpus); public subset approximately 30 hours Audio, Text Transcriptions

Central Kurdish (Sorani) speech corpus developed for automatic speech recognition and related speech-processing research. The full corpus reported in the Jira study contains 43.68 hours of speech from …

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Citation

Abdullah, A. A., & Veisi, H. (2022). Central Kurdish Automatic Speech Recognition using Deep Learning. Journal of University of Anbar for Pure Science, 16(2), 108-118.

Publication Details

DOI 10.37652/juaps.2022.176500

Authors 2 authors

Datasets 1 dataset

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