CONSTRUCTION OF USAGE DIAGRAMS AND MAIN CLASSES OF SOFTWARE FOR SPEECH-TO-TEXT AND TEXT-TO-SPEECH CONVERSION
Keywords:
Speech recognition, Text-to-speech conversion, Flask, LSTM neural network, MFCC, AJAX, jQuery, Translation algorithm, Spectrogram, API integrationAbstract
This article focuses on the development of software that enables real-time speech-to-text and text-to-speech conversion using web technologies. The backend is implemented using Flask, while the frontend utilizes jQuery and AJAX, allowing users to recognize speech and translate it into other languages. LSTM-based neural networks are applied for speech recognition, analyzing audio data using MFCC features. Additionally, spectral analysis and an encoder-decoder model are used for generating speech from text. The application's interface is simple and intuitive, designed to function on various devices
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Copyright (c) 2025 Nurbek Nuritdinov, Narzillo Mamatov
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