Experiments with Machine Learning

Yuriy GutsSolutions Architect

First Things FirstWhat Is Machine Learning?

“ A computer program is said to learn from experience Ewith 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

Categories of Machine Learning

1. Supervised Learning.

2. Unsupervised Learning.

3. Reinforcement Learning.

RegressionPredict a continuous dependent variable

based on independent predictors

Linear Regression

ClassificationAssign an observation to some categoryfrom a known discrete list of categories

Logistic Regression

hypothesis  =  1  /  (1  +  exp(-­‐theta'  *  x));

Logistic Regression: Cost Function

hypotheses  =  sigmoid(X  *  theta);cost  =  (1  /  m)  *  (-­‐y'  *  log(hypotheses)  -­‐ (1  -­‐ y)'  *  log(1  -­‐ hypotheses));

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

‘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.

[cepstra,  aSpectrum,  pSpectrum]  =  MFCC(waveform);

x  =  [cepstra(1);  cepstra(2);  ...;  cepstra(n)];

Let’s code it upMATLAB, logistic regression with conjugate gradient optimization

yuriy . guts  @  gmail . com

linkedin . com / in / yuriyguts

github.com/YuriyGuts/gdg-speech-classifier

Q & A