Phoneme-Based Persian Speech Recognition

TL;DR

This paper presents a method for Persian phoneme recognition using signal processing and deep neural networks, aiming to improve speech recognition accuracy for Persian language.

Contribution

It introduces a novel approach combining STFT and deep neural networks specifically for Persian phoneme detection and classification.

Findings

Effective phoneme detection achieved

High accuracy in classifying Persian phonemes

Potential for improved Persian speech recognition systems

Abstract

Undoubtedly, one of the most important issues in computer science is intelligent speech recognition. In these systems, computers try to detect and respond to the speeches they are listening to, like humans. In this research, presenting of a suitable method for the diagnosis of Persian phonemes by AI using the signal processing and classification algorithms have tried. For this purpose, the STFT algorithm has been used to process the audio signals, as well as to detect and classify the signals processed by the deep artificial neural network. At first, educational samples were provided as two phonological phrases in Persian language and then signal processing operations were performed on them. Then the results for the data training have been given to the artificial deep neural network. At the final stage, the experiment was conducted on new sounds.