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Voice Based Classification of Healthy Subjects and ...
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Voice Based Classification of Healthy Subjects and Patients with Amyotrophic Lateral Sclerosis Suhas BN, Deep Patel, Nithin Rao, Yamini Belur, Pradeep Reddy, Nalini Atcharyam Ravi Yadav, Dipanjan Gope and Prasanta Kumar Ghosh SPIRE Lab, Electrical Engineering, Indian Institute of Science, Bengaluru 560012, India National Institute of Mental Health and Neurosciences, Bengaluru 560029, India PROBLEM STATEMENT To determine the choice of task and device in developing an assistive tool for detection and monitoring of ALS MOTIVATION Need for detection of ALS: Early detection helps better management Monitoring of ALS: Following the progress of the condition [1] CHALLENGES Identifying speech cues that help in better diagnosis [2]. Access for people of different socio-economic backgrounds DATASET Recording setup Moto G5 Plus (MOT), Zoom H6 X/Y recorder (ZOO), Xiaomi Redmi 4 (XIA), Dell XPS 15 (LAP), Apple iPhone 7 (IPH). Sampling freq 44.1 Khz Speech Stimuli Spontaneous (SPON), Diadochokinetic rate (DDK), Sustained Phonation (PHON) Native Language (count) Bengali (5 subjects each), Hindi (5 subjects each) , Kannada (5 subjects each), Odiya (3 subjects each), Tamil (3 subjects each), Telugu (4 subjects each). Total duration SPON (7 hours), DDK (5.36 hours), PHON (7.9 hours) WAVEFORMS AND SPECTROGRAMS (A) subject with ALSFRS-R 0 (B) subject with ALSFRS-R 2 (C) A healthy subject ALSFRS-R RATING FOR SPEECH Severity 0 1 2 3 4 Finding Loss of useful speech Speech combined with nonvocal communications Intelligible with repeating Detectable speech disturbance Normal Scan here for audio samples METHODOLOGY EXPERIMENTS 50 subjects, 5 fold cross validation setup. Per fold : 5 ALS & 5 Healthy Controls balanced for ALS severity, age and language. Classifiers : Support Vector Machines (SVM) & Deep Neural Networks (DNN) Parameters: Choices of N w = {0.5, 0.8, 1, 2, 3}s with N sh = 0.1s. Optimal N w =0.8s. SVM:C& γ selected by maximizing the performance on the validation set DNN: Activation Functions = {‘sigmoid’, ‘tanh’, ‘relu’} Hidden Layers = {1, 2, 3} & No. of Neurons={64, 128, 256, 512} for which the validation loss is minimized. RESULTS 1 2 3 70 80 90 SPON 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 DDK 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 PHON 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 1 2 3 70 80 90 FRAME LENGTH (s) A C C U R A C Y (%) Classification accuracy by varying N w . ? SVM, • DNN. A comparative study on speech classification of ALS vs Healthy controls using 3 speech tasks & 5 recording devices. DDK task consistently performs better than other tasks while MOT performs the best across all devices. 0 5 10 15 20 25 30 35 45 50 55 60 65 70 75 80 85 SPON DDK PHON MOT A C C U R A C Y (%) C O E F F I C I E N T STATIC MFCC VELOCITY MFCC ACCELERATION MFCC Classification results of (3xN) coefficients of suprasegmental features of each mean, median and S.D of one MFCC. SPON: 3 rd , 15 th & 27 th dim of MFCC yield highest accuracy : 78.7%, 82.7% and 84.8% among 36-D MFCC : correspond to 4 th DCT basis function. DDK: 2 nd , 14 th & 26 th dim of MFCC yield highest accuracy : 71.2%, 84.4% and 84.4% among 36-D MFCC : correspond to 3 rd DCT basis function. REFERENCES 1. B.K. Yamini and A. Nalini, ‘Measures of maximum performance of speech related tasks in patients with amyotrophic lateral sclerosis,’ Amyotrophic Lateral Sclerosis, vol. 9, 2008. 2. Aravind Illa, Deep Patel, BK Yamini, N Shivashankar, Preethish Kumar Veeramani, Kiran Polavarapui, Saraswati Nashi, Atchayaram Nalini, Prasanta Kumar Ghosh, et al., ‘Comparison of speech tasks for automatic classification of patients with amyotrophic lateral sclerosis and healthy subjects,’ in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018, pp. 6014-6018. ACKNOWLEDGEMENT Authors thank the Pratiksha Trust for their support. Ongoing work : Developing an assistive tool for detection and monitoring of ALS. http://spire.ee.iisc.ac.in/spire/ [email protected], [email protected]
Transcript of Voice Based Classification of Healthy Subjects and ...
Voice Based Classification of Healthy Subjects and Patients withAmyotrophic Lateral Sclerosis
Suhas BN, Deep Patel, Nithin Rao, Yamini Belur, Pradeep Reddy, Nalini AtcharyamRavi Yadav, Dipanjan Gope and Prasanta Kumar Ghosh
SPIRE Lab, Electrical Engineering, Indian Institute of Science, Bengaluru 560012, IndiaNational Institute of Mental Health and Neurosciences, Bengaluru 560029, India
PROBLEM STATEMENTTo determine the choice of task and device in developing an assistive tool for detection and monitoring of ALS
MOTIVATIONNeed for detection of ALS: Early detection helps better management
Monitoring of ALS: Following the progress of the condition [1]
CHALLENGESIdentifying speech cues that help in better diagnosis [2].Access for people of different socio-economic backgrounds
DATASET
Recording setupMoto G5 Plus (MOT), Zoom H6 X/Y recorder (ZOO), Xiaomi
Redmi 4 (XIA), Dell XPS 15 (LAP), Apple iPhone 7 (IPH).
Sampling freq 44.1 Khz
Speech StimuliSpontaneous (SPON), Diadochokinetic rate (DDK),
Sustained Phonation (PHON)
Native Language (count)
Bengali (5 subjects each), Hindi (5 subjects each) ,
Kannada (5 subjects each), Odiya (3 subjects each),
Tamil (3 subjects each), Telugu (4 subjects each).
Total duration SPON (7 hours), DDK (5.36 hours), PHON (7.9 hours)
WAVEFORMS AND SPECTROGRAMS
(A) subject with ALSFRS-R 0 (B) subject with ALSFRS-R 2 (C) A healthy subject
ALSFRS-R RATING FOR SPEECH
Severity 0 1 2 3 4
Finding
Loss of
useful
speech
Speech combined
with nonvocal
communications
Intelligible
with
repeating
Detectable
speech
disturbanceNormal
Scan here foraudio samples
METHODOLOGY
EXPERIMENTS50 subjects, 5 fold cross validation setup.
Per fold : 5 ALS & 5 Healthy Controls balanced for ALS severity, age and language.
Classifiers : Support Vector Machines (SVM) & Deep Neural Networks (DNN)
Parameters: Choices of Nw = {0.5, 0.8, 1, 2, 3}s with Nsh = 0.1s. Optimal Nw=0.8s.
SVM: C & γ selected by maximizing the performance on the validation setDNN: Activation Functions = {‘sigmoid’, ‘tanh’, ‘relu’}
Hidden Layers = {1, 2, 3}& No. of Neurons={64, 128, 256, 512} for which the validation loss is minimized.
RESULTS
1 2 3
70
80
90
SPON
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
DDK
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
PHON
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
1 2 3
70
80
90
FRAME LENGTH (s)
A
C
C
U
R
A
C
Y
(%)
Classification accuracy by varying Nw. ? SVM, • DNN.
A comparative study on
speech classification of
ALS vs Healthy controls
using 3 speech tasks & 5
recording devices.
DDK task consistently
performs better than
other tasks while MOT
performs the best across
all devices.
0 5 10 15 20 25 30 3545
50
55
60
65
70
75
80
85
SPON
DDK
PHON
MOT
A
C
C
U
R
A
C
Y
(%)
C O E F F I C I E N T
STATIC MFCC VELOCITY MFCC ACCELERATION MFCC
Classification results of (3xN) coefficients of suprasegmental
features of each mean, median and S.D of one MFCC.
SPON: 3rd, 15th & 27th
dim of MFCC yield
highest accuracy :
78.7%, 82.7% and
84.8% among 36-D
MFCC : correspond to
4th DCT basis function.
DDK: 2nd, 14th & 26th dim
of MFCC yield highest
accuracy : 71.2%, 84.4%
and 84.4% among 36-D
MFCC : correspond to
3rd DCT basis function.
REFERENCES1. B.K. Yamini and A. Nalini, ‘Measures of maximum performance of speech related tasks in patients with amyotrophic
lateral sclerosis,’ Amyotrophic Lateral Sclerosis, vol. 9, 2008.
2. Aravind Illa, Deep Patel, BK Yamini, N Shivashankar, Preethish Kumar Veeramani, Kiran Polavarapui, Saraswati Nashi,
Atchayaram Nalini, Prasanta Kumar Ghosh, et al., ‘Comparison of speech tasks for automatic classification of patients
with amyotrophic lateral sclerosis and healthy subjects,’ in 2018 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP). IEEE, 2018, pp. 6014-6018.
ACKNOWLEDGEMENTAuthors thank the Pratiksha Trust for their support.
Ongoing work :Developing an assistive tool for detectionand monitoring of ALS.
http://spire.ee.iisc.ac.in/spire/ [email protected], [email protected]
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