Talk / Overview

In healthcare, speech recognition is a dominant application of deep learning algorithms. The current systems are more involved in words that are proposed correctly and available in the dictionary, however, algorithms to detect and correct errors are still a challenge. This study will seek to develop a framework based on Long Short-Term Memory (LSTM) neural network and Dynamic Time Warping (DTW) algorithm which also enhances performance on speech signals. The developed framework will also address the issue of accuracy as well as word error rate which is a measure of the performance of speech recognition systems. LSTM neural network is known to have the ability to counter the vanishing gradient problem experienced by recurrent neural networks (RNN) whereas Dynamic time warping is effective in calculating the resemblance between two-time series. This study will adopt the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The developed algorithm will be tested and validated using health records thus giving better performance and increased accuracy. 

Talk / Speakers

Lawrence Nderu

Jomo Kenyatta University of Agriculture and Technology

AMLD / Global partners