A Spell-checker Integrated Machine Learning Based Solution for Speech to Text Conversion
H. M Mahmudul Hasan, Md. Adnanul Islam, Md. Toufique Hasan, Md. Araf Hasan, Syeda Ibnat Rumman, Md. Najmus Shakib · 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) · 2020
Speaking is undeniably an effective way of communication in any language, which is now an essential feature in various sectors of technology. This study proposes to build such a speech to text conversion system for the Bengali language because Bengali, being one of the most popular languages worldwide, is very little explored in this research arena. Hence, the study aims to provide a platform to detect speech and recognize its content as text automatically. This work promises to help the mass people who may not be educated enough to write fluently and accurately. In this study, the focus is on processing Bengali speech data using ‘DeepSpeech’, which creates a neural network to recognize the audio files containing speech and then, to transform the audio speech into its text format. Moreover, the performance evaluation of the proposed system is accomplished after integrating it with the proposed Bengali spell corrector mechanism to achieve significant improvement over the existing approach.