Speech recognition of deaf and hard of hearing people using hybrid neural network
C. Jeyalakshmi, Vijaykumar Krishnamurthi, Adhvikaa Ambikapathi Revathi · 2010
This paper describes isolated word recognition of deaf students by unsupervised and supervised neural network. Compared to normal speech, there is high variability in deaf speech and by hearing once we couldn't understand it. By the use of proposed method deaf people can make use of all voice operated devices. In this paper we use combination of SOFM and BPN neural network for recognition. Initially the input is sampled, filtered, windowed and Perceptual Linear Predictive Coefficients are determined for each frame. These coefficients are applied as input to the SOFM neural network. The output of this network is given to BPN neural network comprising of 3 layers for learning. The network has been trained with five words uttered by five different deaf persons in the age group of 5-10 years. Another set of same five words uttered by same five deaf persons were used for test purposes. The recognition results for the word one, three, four is 50 to 60% and for five is 50%...But the recognition results for word two are only10% since the variability is high for two. The results can be improved by varying the parameters of the hybrid neural network.