Week 4 project demonstration

1. Phoneme-Wise Speech Impairment
Diagnosis from Audio Data
Abdullah Al Rashid
February 2nd, 2018
Insight Data Science

2. Speech Pathology Workflows Will Benefit from
Intelligent Automation
Current state:
Subject
Pathologist 1
Pathologist 2
Pathologist 3
● ~40 hours!
● $$$

3. Speech Pathology Workflows Will Benefit from
Intelligent Automation
Current state:
Subject
Pathologist 1
Pathologist 2
Pathologist 3
● ~40 hours!
● $$$
Proposed solution:
Subject Technician

4. Speech Pathology Workflows Will Benefit from
Intelligent Automation
Current state:
Subject
Pathologist 1
Pathologist 2
Pathologist 3
● ~40 hours!
● $$$
Proposed solution:
Subject Technician

5. Speech Pathology Workflows Will Benefit from
Intelligent Automation
Current state:
Subject
Pathologist 1
Pathologist 2
Pathologist 3
● ~40 hours!
● $$$
Proposed solution:
Subject Technician + Pathologist

6. Speech Pathology Workflows Will Benefit from
Intelligent Automation
Proposed solution:
Subject Technician + Pathologist
● Fewer
hours
● $ savings
Current state:
Subject
Pathologist 1
Pathologist 2
Pathologist 3
● ~40 hours!
● $$$

7. Impairment Changes Intrinsic Phoneme
(Dis)Similarities
ba
la
Unimpaired
Unimpaired

8. Impairment Changes Intrinsic Phoneme
(Dis)Similarities
ba
la
ba ∦ la
Unimpaired
Unimpaired

9. Impairment Changes Intrinsic Phoneme
(Dis)Similarities
ba
la
ba ∦ la
bla
Unimpaired
Unimpaired
Impaired

10. Impairment Changes Intrinsic Phoneme
(Dis)Similarities
ba
la
ba ∦ la bla ∥ la
bla
la
Unimpaired
Unimpaired Unimpaired
Impaired

11. Impairment Changes Intrinsic Phoneme
(Dis)Similarities
ba
la
ba ∦ la bla ∥ la
bla
la
Unimpaired
Unimpaired Unimpaired
Impaired
Phoneme 1 Phoneme 2

12. Impairment Changes Intrinsic Phoneme
(Dis)Similarities
ba
la
ba ∦ la bla ∥ la
bla
la
Unimpaired
Unimpaired Unimpaired
Impaired
Phoneme 1 Phoneme 2
Power
spectrum 1,
p1
(ω)
Power
spectrum 2,
p2
(ω)

13. Impairment Changes Intrinsic Phoneme
(Dis)Similarities
ba
la
ba ∦ la bla ∥ la
bla
la
Unimpaired
Unimpaired Unimpaired
Impaired
Phoneme 1 Phoneme 2
Power
spectrum 1,
p1
(ω)
Power
spectrum 2,
p2
(ω)
Similarity{p1
, p2
}

14. Random Forests (RFs) to Compare Test Subject
Speech Against Unimpaired Speech
Phonemes missing
for subject
Unimpaired
reference (rows)
Subject
(columns)

15. Random Forests (RFs) to Compare Test Subject
Speech Against Unimpaired Speech
Phonemes missing
for subject
Unimpaired
reference (rows)
Subject
(columns)
● RFs to classify subjects into
impaired vs. unimpaired
○ Works for small sample size
○ Short training times
○ Feature importance estimates

16. Random Forests (RFs) to Compare Test Subject
Speech Against Unimpaired Speech
● RFs to classify subjects into
impaired vs. unimpaired
○ Works for small sample size
○ Short training times
○ Feature importance estimates
● Results: ROC reasonable, but
model overfitted
Receiver Operating Characteristics (ROC) for
RF Classifier

17. Random Forests (RFs) to Compare Test Subject
Speech Against Unimpaired Speech
● RFs to classify subjects into
impaired vs. unimpaired
○ Works for small sample size
○ Short training times
○ Feature importance estimates
● Results: ROC reasonable, but
model overfitted
Learning Curves for RF Classifier

18. Articul8: A Demonstration
https://articul8-diagnostics.herokuapp.com
+
Audio files
Classification

19. Both Pathologist and Patient Can Save Time on
Diagnosis and Focus on Remediation Early

20. Abdullah Al Rashid: Physicist, Software Engineer,
Data Scientist
B.A.Sc. -- Engineering Science
Ph.D. -- Physics
abdullah-alrashid
NearIdentity

21. Articul8 Offers Test Suggestions for Speech
Pathologist Within Patient Referral

22. Articul8 Offers Test Suggestions for Speech
Pathologist Within Patient Referral
● Phonemes for further testing
○ Top 20 RF features
App Screenshot: Subject Classification

23. Articul8 Offers Test Suggestions for Speech
Pathologist Within Patient Referral
● Phonemes for further testing
○ Top 20 RF features
App Screenshot: Subject Classification

24. Similarity Metric for Power Spectra: Hellinger
Proximity of “Probability Distributions”
● Hellinger distance:
DH
{p1
, p2
} = 1- ∫ [p1
(ω) x p2
(ω)]1/2
dω}
● Hellinger “proximity”:
PH
{p1
, p2
} = 1 - DH
{p1
, p2
}
Phoneme 1 Phoneme 2
Power
spectrum 1,
p1
(ω)
Power
spectrum 2,
p2
(ω)
Similarity{p1
, p2
} = PH
{p1
, p2
}

25. [Pending] Feature Weights from RF Classifier