An End-to-End Deep Learning Model to Recognize Farsi Speech from Raw Input

Automatic speech recognition systems usually solve the problem of recognizing speech by dividing the problem into different independent stages. First, they extract speech features and then use an acoustic model to reach the phoneme probabilities and from those probabilities, they reach sequence of recognized words. Recent advances in technology, especially in the area of deep neural networks in combination with speech recognition, shows that this division is not necessary and we can reach sequence of alphabet letters straight from the raw signal. In this work, we implemented and tested an endto- end convolutional neural network system with raw input for Farsi speech recognition and then compared its performance to another system that uses MFCC features. We show that using an end-to-end system with our configuration,which reaches series of phonemes from raw speech works better for Farsi speech as well as for English.