CONSTRUCTION OF USAGE DIAGRAMS AND MAIN CLASSES OF SOFTWARE FOR SPEECH-TO-TEXT AND TEXT-TO-SPEECH CONVERSION

Nuritdinov, Nurbek, Mamatov, Narzillo

Al-Farg'oniy avlodlari · 2025-yil

Annotatsiya

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

Maqola ma’lumotlari
MualliflarNuritdinov, Nurbek, Mamatov, Narzillo
JurnalAl-Farg'oniy avlodlari
Nashr sanasi2025-03-23
Son1
Betlar29-34
TilRus

Kalit so‘zlar

Распознавание речи, Конвертация текста в речь, Flask, Нейронная сеть LSTM, MFCC, AJAX, jQuery, Алгоритм перевода, Спектрограмма, API-интеграция, Speech recognition, Text-to-speech conversion, Flask, LSTM neural network, MFCC, AJAX, jQuery, Translation algorithm, Spectrogram, API integration, Nutqni tanib olish, Matndan nutqqa aylantirish, Flask, LSTM neyron tarmog’i, MFCC, AJAX, jQuery, Tarjima algoritmi, Spektrogramma, API integratsiyasi

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