Speech Recognition Using Shallow Neural Network Catogorisation

Running head: Speech Recognition Using Shallow Neural Network Classification
Speech Recognition Using Shallow Neural Network Classification
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Speech Recognition Using Shallow Neural Network Classification1
Introduction to research problem:
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. This is also known as the automatic speech recognition, speech to text
conversion or computer speech recognition. The typical speech recognition works by training
a machine by isolated words or vocabulary as spoken by a person with proper accent. The
system typically analyse the voice of the particular person and then fine tune’s voice for
increasing the accuracy of recognition. This type of speech recognition system is known as
speaker dependent speech recognition where a sample voice is used for training and the
systems with no training voice are known as speaker independent speech recognition system.
There are different applications of speech recognition that includes voice user interfaces like
voice dialling, call routing, domotic appliance control and key word searching. However, 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 (Yu and Deng
2016). Hence, for this project a relevant data will be used that contains the voice sample
attributes of Parkinson’s disease patients and voice sample of healthy patient which will be
analysed using neural networks in the software that outputs whether a person/s is diseased or
normal. Hence, this is a speaker dependent speech recognition system where the voice sample
of healthy and diseased patients will be used for training the algorithm with neural network
and then will be tested on a set of people combining both healthy and diseased subjects.

Speech Recognition Using Shallow Neural Network Classification2
Scope of interest:
The particular scope of interest of this of this project is to detect patients with
Parkinson’s disease without doing their medical test. This is very much helpful as this
reduces the cost of medical testing and time for Parkinson’s test and quick medication can be
provided the patients. Parkinson’s disease is one type of nervous system disorder which
affects mainly the movement of the individuals. The symptoms of Parkinson’s disease
gradually increases, at an early stage a tremor can be felt at just one hand. In the later stage
the disease causes stiffness or slow movement and reflex. There is no permanent cure for the
Parkinson’s disease but proper medication can help patients to significantly improve their
symptoms (Ascherio and Schwarzschild 2016). The Parkinson’s disease has several
symptoms like tremor, bradykinesia, rigid muscles, impaired balance and posture, reflex loss,
changes in voice, changes in hand writing. Thus this project aims to provide medical
assistance to the patients by detecting Parkinson’s disease at an early stage only checking the
voice sample of the person and hence benefits many of the individuals who are not able to
find medical testing facility near their residence or who are not able to afford the cost of
testing.
Dataset introduction:
The dataset which will be used in the software to identify voice samples as
Parkinson’s diseased positive or negative are retrieved from the UCI machine learning
repository Parkinson Speech. In the training data of the dataset there are voice sample
characteristics of 20 PD(Parkinson’s disease) patients and 20 healthy individuals are included
who appeared in the survey of Neurological department of Cerrahpasha at Istanbul university
(UCI Machine Learning Repository: Parkinson Speech Dataset with Multiple Types of Sound
Recordings Data Set 2020). All the healthy and PD patients are instructed to speak multiple