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Article · Neliti · August 1, 2025

Methods and Algorithms for Speech-to-text Conversion

N. (Narzullo) Mamatov, Nurbek Davlataliyevich Nuritdinov

Nurbek Davlataliyevich Nuritdinov
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Abstract

This thesis presents the fundamental methods of automatic speech-to-text (ASR) technologies. Firstly, the Dynamic Time Warping (DTW) method, which identifies similarities between signals, is described. Then, Hidden Markov Models (HMM) and their application in modeling probabilistic sequences of states are explained. The role of Gaussian Mixture Models (GMM) in acoustic modeling and their function in matching speech features to phonemes are highlighted. The advantages of neural networks in speech recognition, including their ability to process in parallel and generalize patterns, are discussed. Furthermore, modern approaches based on Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs) are shown to improve the quality of speech recognition. Language models and adaptation methods are also reviewed. These approaches are of significant importance in improving speech recognition systems for the Uzbek language.

Speech Recognition and SynthesisSpeech and Audio ProcessingSpeech and dialogue systemsspeech-to-textspeech recognitiondynamic time warping

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