Lviv GDG Meetup presentation. From zero to human speech recognition.

1. Experiments with Machine Learning
Yuriy Guts
Solutions Architect

2. First Things First
What Is Machine Learning?

3. “ A computer program is said to learn from experience E
with respect to some class of tasks T and performance measure P,
if its performance at tasks in T, as measured by P,
improves with experience E.
— Tom M. Mitchell

4. Categories of Machine Learning
1. Supervised Learning.
2. Unsupervised Learning.
3. Reinforcement Learning.

7. Regression
Predict a continuous dependent variable
based on independent predictors

11. Linear Regression

15. Classification
Assign an observation to some category
from a known discrete list of categories

16. Logistic Regression
hypothesis = 1 / (1 + exp(-­‐theta' * x));

17. Logistic Regression: Cost Function
hypotheses = sigmoid(X * theta);
cost = (1 / m) * (-­‐y' * log(hypotheses) -­‐ (1 -­‐ y)' * log(1 -­‐ hypotheses));

18. Let’s classify human speech!
Decide whether a spoken phrase contains the word ‘Google’ or not

19. ‘Google’ Detector: Feature Mapping
Options for building X[ ]:
Input: Audio file (WAV, 16 bit mono, 44.1 kHz)
Output: 1 if it contains the word ‘Google’, otherwise 0
1. Use raw waveform as a feature vector.
But: will have 66150 features for a 1.5 second file.
Kinda scary, and easy to overfit.
2. Use Mel-Frequency Cepstral Coefficients (MFCC).
Believed to be closer to human auditory response.
Depending on parameters, can give about 80 features per file.

20. [cepstra, aSpectrum, pSpectrum] = MFCC(waveform);
x = [cepstra(1); cepstra(2); ...; cepstra(n)];

21. Let’s code it up
MATLAB, logistic regression with conjugate gradient optimization

22. yuriy . guts @ gmail . com
linkedin . com / in / yuriyguts
github.com/YuriyGuts/gdg-speech-classifier
Q & A