Assessment of dysarthric speech using Elman back propagation network (recurrent network) for speech recognition

In this paper, the role of speech recognition system in the assessment of dysarthric speech based on a method called Elman back propagation network (EBN) is studied. Dysarthria is a neurological disability that damages the control of motor speech articulators. The persons who suffer from Dysarthria may have speech intelligibility rate which may vary from low (2 %) to high (95 %). EBN is a Recurrent network, here a fully connected neural network is built such that the speech characteristics are represented simultaneously by neuron activation states. It is an efficient self supervised training algorithm. For parametric representation of the speech signal, we used Glottal feature along with mel frequency cepstral coefficients. Then finally the output of both the features is compared after the evaluation process using different neural networks and modeling methods. Evaluation of the proposed method is done on the subset of the Universal Access Research database. The subset consists of 9 dysarthric speakers out of 19 speakers each uttering 100 words repeatedly 3 times. The promising performance of the proposed system can be successfully applied to help the people who work for the voice disorder persons.