Speech Recognition System Based on Machine Learning in Persian Language

In today's world, where speech recognition has become an integral part of our daily lives, the need for systems equipped with this technology has increased dramatically in the past few years. This research aims to locate the two selected Persian words in any given audio file. For this purpose, two standard and native datasets were prepared for this model one for train and the other for the test. Both datasets were converted into images of audio waveforms. Using the object detection technique, the model could extract different bounding boxes for each test audio, and then each box image goes through a CNN classifier and returns a corresponding label. Finally, a threshold is set so that only boxes with high accuracy are displayed as output. The results showed ۹۳% accuracy for the CNN classifier and ۵۰% accuracy for testing the model with object detection.

کلیدواژه ها:

Speech recognition ، Signal processing ، object detection ، Neural Network ، Deep Learning