Sentiment Analysis - Voice Analysis for PD Detection

8/12/2019 Sentiment Analysis - Voice Analysis for PD Detection

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Research Methodology Proposal

Prepared by: Norhasmizawati Ibrahim (813750)


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Sentiment Analysis using Voice

AnalysisEarly Detection of

Parkinsons Disease

Presentation Outline

1. Introduction

2. What is the research allabout?

3. What is the research

question?

4. Why this research isimportant?

5. How to answer all these

questions?


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Background of Study

Sentiment analysis Widely used to identify and extract particular information

from both live and recorded conversations.

Part of speech analyticemphasize on emotional states

Different method of scaling been used to analyze the

common words that have correlation to negative, positive

or natural sentiment

Important

to define the attitude of human when they speak or write

on certain issues. Information extracted perhaps deliver meaningful

information.

Developing a prevention of disease.


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Problem Statements

Parkinsons Disease (PD) refer to nerve cell in themidbrain having slow progression. Often causing

symptom like tremor/shaking, slow movement and

stiffness. About 1 in 500 people has Parkinson's and

15K20K patients. Individual at risk60 years andolder. Causesdont exactly know why people get

Parkinson's and how to prevent it.

Factsresearches on voice analysis has shownvaluable finding to detect Parkinsons disease at early

stage. The lacking of voice database and some

research findings not clear since not applied to

pathologic yet.


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Significant of Study

Important to find out

Scope of data set for analyzing

Analyzing voice pattern and classification

Modelling method for early detection Accuracy of the voice diagnose result

Benefitted

Clinicalbiomarker for PD disease detection

Individual with PDto get earlier treatment

Normal individualearly detection


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Research Questions (RQ)

1. How to detect voices changes of early

stages of PD?

2. How to analyze the various voice

pattern?

3. What is the impact of the voice sentimentanalysis to related domain?


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Research Objective (RO)

1. To identify changes of voice and

articulation at early stages of PD

patients?

2. To determine the most effective method

for early detection of PD?

3. To evaluate the method performance in

terms of the accuracy?


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Introduction

Research on voice analysis is used

extensively in order to detect disease that

affected to voice disorder.

Inspired researchers to determine the most

effective indicator for detection.

Methods used will be describe later.


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Automatic Detection

The automatic detection for voice impairment has been carriedout in short-term frame basis using Multilayer Perceptron and

Learning Vector Quantization. Based on observation done to

vocal folds, LVQ is more reliable to parameterized voice

disorder detection with 96%.

Using artificial techniques, Optimum-Path Forest techniques

was introduced which not assuming any shape or separability

of classes or feature space[12]. On the other hand, OPF alsofree of parameter and run training phase faster than others.

Even though, if considering standard of deviation, OPF appear

to be similar to others.


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Classification Features

Performed to identify the most important features to

differentiate between normal speaker and speakers with

early stage of PD.

By performing a correlations-based feature selection foracoustic and vocal modelling, the result are promising with

rate for both is 88% and 79%. The result expected to

improved after performing feature selection [13].

However, besides voice, articulation and prosodic

evaluations, other feature like energy pauses and F0 also

need to consider. The research found that masses and the

compliances of spring as the most important parameters in

two- mass vocal fold model [13].


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Classification Features

Other research, suggest Intrinsic Mode Functions supported byFuzzy Set Classifier to differentiate. The experiment , 99% of

the participant are successfully classified.

In 2012, there also research to analyze the characteristic ofspeech. By recording /ah/ phonation from136 respondent, 16

features have been extracted using students t-test. For further,

two types of ANN (MLP and RBF) are used for classification.

RBF showing good classification compared to MLP.


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Conclusion

The most effective method will be selected for the

implementation

Even OPF is provided with more advantages and result

outperformed but if considering the standard deviation, it issimilar to other techniques.

MFCC and SVM will be utilized for this research implementation

due to both are non-invasive method, fast, easy to use, less

computational intense and affordable for the clinicians.


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Research Design

Purposeto provide answer to the method

proposed as accurate as possible

Type of study

Experimental approachto identify and determine

which method is appropriate for early detection of PD.

Method used

Quantitative approachapply systematic process

utilizing data to test the following research questions.


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Population and Sampling

Important step in conducting a research. Probability samplingsample is

randomly select from the population.

Populationhealthy and unhealthy

people

Population Gender Age Ethnic Total

Healthy Male 46-85 Malay, Chinese, Indian 30

Female 46-85 Malay, Chinese, Indian 30

Unhealthy Male 46-85 Malay, Chinese, Indian 30

Female 46-85 Malay, Chinese, Indian 30


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Data Collection

Use primary data

Specifically to address the problem in question.

Most voice database not classifying ethnic.

Voice collected will provide different vocal in terms of vocalfold that typically considered nearly periodic in healthy

voices [15].


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Decision

(normal/abnormal)

FeaturesClassification

Acoustic FeaturesExtraction

Data Analysis / process

Voice SignalProcessing

Capturing

voices

Flow of research implementation

LOGO


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Cont...

Extraction processto identify the best parametricrepresentation for voice detection using MFCC

Feature extraction of MFCC computation


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Cont...

SVM classifierto differentiate each of the voice pattern

identified.

WEKA toolkit will be use for signal classification.

Feature selection will be performed in order to identify

healthy and unhealthy voice signal especially to detect PD

at early stage.


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Documents

Sentiment Analysis - Voice Analysis for PD Detection