Speech Recognition Using Shallow Neural Network Classification

Speech Recognition Using Shallow
Neural Network Classification
Introduction:
Speech recognition is a subfield of computer linguistics by which
methodologies are developed by which enables the machines to
recognize the spoken language by human by converting into text.
In this particular research a different type of speech recognition
application is performed where the by different attributes of speech a
person is identified as a healthy or diseased. In particular the
recognition software will be able to recognize when a diseased person
(for a particular disease) speaking to the system by analysing its
attributes or when a healthy person is speaking. Now, instead of
considering multiple disease detection the software will be
implemented for only one disease which is chosen to be Parkinson’s
disease where the patient goes through voice change with other
change in physical attribute change.

Problem definition:
The problem can be defined as building an automatic
artificial intelligence system that can classify a person as
health or affected by Parkinson’s disease when the key
attributes of the voice sample is given as input to the
system in appropriate format.

Dataset information
Parkinson Speech Dataset with Multiple Types of Sound
Recordings Data Set.
Available at:
https://archive.ics.uci.edu/ml/datasets/Parkinson+Speech
+Dataset+with++Multiple+Types+of+Sound+Recording
s#
Number of instances: 1040
Number of variables: 26
Variable type: Integer, numeric
Different test and train set

Methodology:
Entire dataset containing training and testing data are
loaded in a programming software MATLAB.
MATLAB is chosen as this has many inbuilt libraries and
functions for neural networks and artificial intelligence
which are needed for training and testing with the chosen
Parkinson’s data.
Different algorithms are applied with different
parameters such as number of hidden layer and initial
weights of bias vectors. The algorithm with best
performance with optimal parameter is chosen for
classification.

Experiments and discussion:
Many testing of the initial neural network is performed in
sequence to improve the performance on test case.
Chosen performance function: mean square error
Optimum performance with: Bayesian Regularization
back-propagation method (‘trainbr’)
Hidden layer size = 50

Bayesian regularization
algorithm:
Bayesian regularized ANN are more efficient than the
normal back-propagation nets and thus the need of
lengthy cross-validation can be reduced by this method.
Main advantage of algorithm: Robust and validation not
required which save O(N^2) time complexity.
Overtraining in this method is very difficult as an
objective Bayesian criterion is required to stop the
training. The over fitting to the data is also quite unlikely
as ‘trainbr’ calculates and then trains on a wide variety of
network parameters and weights of each node that
eliminates the nodes which are not relevant.