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Cenk Demiroglu - Analysis of Prosodic Patterns in Conversational Speech in People with Alzheimer’s Disease
1. Analysis of Prosodic Patterns in
Conversational Speech in People with
Alzheimer’s Disease
Dr Cenk Demiroğlu
Assistant Professor at Özyeğin University, Istanbul, Turkey
Founder and CEO of NeoSes Inc. Istanbul, Turkey
2. Who are we?
Özyeğin University
Private school in Istanbul, Turkey
Has one of the best faculty profiles in the nation
OzU Speech Lab
Founded and directed by Cenk Demiroğlu
Experts at
Speech recognition, synthesis and verification technologies
Tightly connected to industry (including AT&T Research in the USA)
Neoses is a spin-off company from the lab
Focused on speech technologies and machine learning theory
3. Why are we Interested in Biomedical?
Health is one of the emerging areas in the speech field
We are interested in
Alzheimer’s diagnosis and monitoring
Depression diagnosis and monitoring
Increasing interest in the EU framework programmes
Increasing interest in the Turkish Ministry of Health
We see opportunities
For research
For commercialization
4. Our Core Strategy
Diagnosis can be semi-automated
Need to work with Medical Doctors and hospitals
Diagnosis of the doctor is still critical
Technology may help doctors
There is a large gap between doctor visits (may be months)
Not enough MDs
Too expensive to monitor very closely
Speech technology can help
Speech analysis over the phone
Cheap monitoring through automated call centers
5. Literature Review
Cognitive decline in the auditory part of the central nervous
system has been observed in Alzheimer’s disease
Lexical problems are one of the earliest signs of the disease
Early detection
Prosodic parameters have been found to be relevant
identifying the disease
Research in analysing spontenous, conversational speech is
relatively rare.
Semantic and syntactic analysis
6. Focus on Conversational Speech
Spontenous speech is rich in information
Prosodic
Semantic
Syntactic
Literature is not multi-disciplinary (at least not enough)
A combination of expertise in speech signal processing, pattern recognition ,and medical
fields is required
Some of the most powerful pattern recognition and cluster analysis algorithms have not
been investigated enough in the literature (Graphical Models, probabilistic factor analysis
etc.)
Some of the more advanced speech analysis tools have not been used (accurate glottal
closure point analysis using STRAIGHT)
Results of the efforts with the speech recognition based approach to lexical deterioration
analysis mainly missing
The idea is there but results are not. Difficult problem!
Leading labs in the speech research are just beginning to get interested with this problem
We have a brief summary of our preliminary investigation here
7. Data Collection Method
First attempt:
Setup an automated call center
Patients call everyday
List of 20 questions
What time did you wake up today?
What did you eat at breakfast?
Did you do any exercise?
Etc
Problems
Patients forget to call!
Patients are not able to understand and respond to the question over the
phone
8. Data Collection Method
Second attempt:
Send a graduate student to the nursing house
The student interviews the patient
Data is collected through a digital voice recorder
Collected data from 24 patients at phase-3 (late phase of the
disease)
Need more data from more patients at different phases
Need to monitor over time
We have data from 400 healthy subjects
The age range is 30-50
Need better age range for a fair comparison
9. Subjective Observations
We could not understand what some of the patients were
saying
Slurred speech
Semantic and syntactically wrong sentences
Missing and/or mispronunced phonemes
In some patients answers to the questions were relevant but
the patients begin to repeat himself/herself after a couple of
minutes
If the answers are relevant, they are typically short
If the answers are irrelevant, they are mostly long
10. Prosodic Parameters and KL Distances
Feature KLD
Silence/speech ratio 7.9123
Speech (max. distance to median) in sec 6.1105
Speech (std. dev) in sec 5.1559
Number of silence per minute 2.7054
Silence (std. dev) 2.3291
Speech (mean) in sec 2.2849
Pitch (max. distance to median) 1.9820
Pitch (min. distance to median) 1.9359
KLD: Kullback-Leibler distance between distributions. A way to measure the discriminatory
power of the features
11. Prosodic Parameters and KL Distances
Feature KLD
Silence (mean) 1.9013
Silence (median) 0.8603
Silence (distance of min to median) 0.8603
Silence (max. distance to median) 0.6320
Speech (median) in sec 0.4450
Speech (min dist 2 median) in sec) 0.4450
Pitch (std. dev) 0.3260
17. Conclusion and Future Work
Preliminary study is promising
Even individual features have high discriminatory power
Need more data to use more advanced analysis techniques
Need data from multiple phases of disease to get a stronger
sense of correlation
Need to follow the patients over time to monitor how the
parameters change over time
We made an attempt to do classifier to fusion to improve the
performance but no success yet
Will focus more on this in the future