Continous Speech Recognition Using Long Term Memory Cells
Aby Abraham · OhioLink ETD Center (Ohio Library and Information Network) · 2013
The thesis proposes a continuous speech recognition model using neural network structure which was inspired by long term memory model of human cortex.Speech recognition model extracts and selects the finest representation of speech signal using mel-frequency cepstrum coefficients.The extracted features are fed to neural network with long term memory (LTM) cells which learns the sequence.The LTM cells have the capability to address three main issues of sequence learning including error tolerance, significance of elements and memory decaying which are used to tune the LTM model with parameters that depends on the environment it learns.To validate the model, two datasets have been used -spoken English digits and spoken Arabic digits in speaker dependent mode.The parameters of LTM model have been optimized based on the environment.The results show that the LTM model with fine tuning of parameters is 97% accurate in recognizing the spoken English digits datasets, and 99% accurate in recognizing spoken Arabic digits datasets.First, I would like to offer my sincere thanks to Dr. Janusz Starzyk for being my advisor and helping me through all my research with