Indian Journal of Science and Technology
Year: 2017, Volume: 10, Issue: 40, Pages: 1-7
Syed Abbas Ali1 *, Sallar Khan2 , Humaira Perveen1 , Reham Muzzamil1 , Mahnoor Malik1 and Faiza Khalid1
1NED University of Engineering and Engineering and Technology, Main University Road، Karachi 75270, Pakistan; [email protected], [email protected], [email protected], [email protected], [email protected] 2Sir Syed University of Engineering and Engineering and Technology, Karachi, Pakistan; [email protected]
*Author For Correspondence
Syed Abbas Ali
NED University of Engineering and Engineering and Technology, Main University Road، Karachi 75270, Pakistan; [email protected]
Urdu language of Pakistan has more than 100 million speakers in Pakistan, India, Afghanistan and Middle East. With low English literacy rate average Urdu speaking person faces barriers in communicating with foreign people in terms of accessing information, carrying business. This paper proposes an interactive Urdu to English language speech translator using deep Neural Network. ASR module in proposed pipeline is composed of deep neural network and is simpler as compared to traditional ASR which requires complex hand engineering like feature extraction and resources like phoneme dictionary. It was clearly seen that the proposed model shows the high accuracy when the input is recorded audio and it shows poor performance with real time input. While one HTTP request per input transcription produced English translation for Text to Text translation using Python Text Blob library. The final output was achieved with a delay of no more than 30 seconds. Furthermore, we have tested and provided some statistical findings, the result shows that value updating for neural network layer’s bias, standard deviation when Adam optimizer parameters are set as follows: beta1=0.9, beta2=0.9 and learning rate =0.01 meanwhile dropout rate was kept to 5% to offer regularization and observed value for scalar maximum lies between 0 and 0.08. There is a little deviation at 0.05 step, value decreases and afterwards that bias maximum scalar increases with positive values and finally increases exponentially at later stages of training further results are discussed in experiment section respectively. The proposed speech recognition model out performs traditional automatic speech recognition systems in efficiency, simplicity and robustness.
Keywords: Deep RNN, Language Translator, N-gram LM, Text Blob Translation, Urdu Language, ASR
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