Detecting Intoxicated Speech - Columbia Universitydgw2109/presentation.pdf · ALC – Alcohol...
Transcript of Detecting Intoxicated Speech - Columbia Universitydgw2109/presentation.pdf · ALC – Alcohol...
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Detecting Intoxicated Speech
Daniel Wilkey John Graham CS6998
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Given speech, was the speaker intoxicated?
Interspeech 2011 Intoxication Challenge
Application for field sobriety testing, ignition-guards
Background
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ALC – Alcohol Language Corpus
162 total participants: 84 male, 78 female
Participants reached a BAC .28 – 1.75
Read 15 minutes of intoxicated speech
Returned 2 weeks later
Read 30 minutes of sober speech
The Corpus
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5400 samples in total, 75 per person
Divided into 3 sets:
Development, Training, Test
Development & Training are labeled with 4368 features
Used cross validation to obtain results
The Corpus p2
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Shrikanth Narayanan of UCLA
Global speaker normalization
Normalizing by the sober class
Relative improvement of 7.04% overall
Professor Hirchberg
Phonotactic and phonetic cues
Experiment tests un-weighted average recall… why?
We chose f-measure
Includes recall and precision
Prior Research
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Remove extraneous features with WEKA
Info-gain ratio algorithm
MFCC features performed well
No F0-based features near the top
Experiment Preparation
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Ignore test set
unlabeled
Down-sampling the training set
Achieved 50/50 ratio of alcoholised to non-alcoholised speech
Experiment Preparation
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Global Speaker Normalization (Narayanan) Insignificant negative change
Sober class normalization (Narayanan) Insignificant negative change
Gender class normalization Insignificant positive change
Combining global speaker with gender normalization 10.75% relative improvement in f-measure
Poor performance potentially related to some F0 features being filtered out
Normalization Attempts
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Tried retesting data with fringe cases omitted
Fringe case BAC between .08% and .16% proposed by Batliner
We tried .02% to .08%
Difference in data set and threshold
Relative decrease of F-measure by 3.25%
On the Fringe
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Machine Learning Optimizations
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Optimizing the SVM
Varied polynomial kernels
Radial basis function (RBF)
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Varying number
Folds
Iterations
Optimization Techniques
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Configuration
SVM kernel n=3
10-fold cross validation
Gender normaliation
Sober class normalization
Final Results Difficult to compare!!
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Difficult to compare results
Need better corpus
Extend with GMM super-vectors
Conclusions / Extensions