CS578: Project on Gaussian Mixture Model (GMM), and April 12th, 2016

CS578: Project on Gaussian Mixture Model (GMM), and April 12th, 2016
CS578: Project on Gaussian Mixture Model (GMM), and
Speaker Identification with GMM
April 12th, 2016
Delivery: End of Semester
Questions: [email protected], [email protected]
During this project you will develop an automatic speaker identification system. More specifically the identification system is split into two modules; the features extraction module and the
classification or machine learning module which you will develop.
Here are the steps.
1. Material of the project:
For this project you will use two data sets; one for training the classification method and one for
testing. Dataset 1 or training set consists of 20 male and 20 female speakers with 9 sentences
each one. Dataset 2 or testing set will be used for the evaluation of the performance of the
system you will develop.
2. Features Extraction Module:
The features that you will extract from the speech signals are the Mel-scale Frequency Cepstral
Coefficients (MFCCs). See
http://en.wikipedia.org/wiki/Mel-frequency_cepstral_coefficient
for a description of these coefficients.
You had also a lecture where the computation of
these coefficients was provided. You can develop your own Matlab function or you can obtain a free version from an auditory/speech/voice toolbox. For instance, in Matlab central,
http://www.mathworks.com/matlabcentral/index.html, you will find various implementations of
MFCC. For example, you may want to check the following (look for mfcc in the provided
table)
http://www.mathworks.com/matlabcentral/fileexchange/?term=tag%3A"mfcc"
where some interesting applications have also been developed (i.e., devise control using speech).
Create a Matlab function which performs the feature extraction from a speech signal and saves
the features in a .mat file. As a suggestion, you may use 20ms frame size and 5ms time step.
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3. Training:
For each provided speaker, create a Gaussian Mixture Model (GMM) using the corresponding MFCCs features. The estimation of the GMM parameters is performed through the
Expectation-Maximization (EM) algorithm. For developing your own EM algorithm you need
to follow the steps provided during the corresponding lecture. Also, a tutorial paper was also
provided to you. Alternatively, you can use any GMM-EM optimization toolbox which is
available from the internet. Again, Matlab central is a rich source of implemented algorithms.
It is highly recommended to apply GMM-EM optimization algorithm to synthetic examples.
Thus, firstly create a Matlab function that trains a GMM using EM for synthetic data. It
should plot the clusters of GMM. Then, develop a Matlab function that loads all the MFCC
features of a speaker and creates a GMM model for that speaker. Save the parameters of GMM.
Do the same for all speakers.
Write down briefly how EM algorithm operates and what model parameters you use (i.e. how
many Gaussians are used in the GMM, is the covariance matrix of the Gaussians diagonal, how
EM is initialized).
4. Testing:
Bayesian criterion and especially Maximum a Posteriori (MAP) will be used to discriminate
between the speakers. MAP says that an input speech signal belongs to speaker X, if this
particular speaker X, has the largest a posteriori probability given the input signal, which is
equivalent to the largest likelihood when all a priori probabilities are equal. Thus, given a
speech signal from the pool of speakers for which training was performed, try to identify to
whom this signal belongs to.
Report the performance of your speaker identification system. Is it able to discriminate all the
speakers or not?
5. Experiments:
Construct two speaker identification modules with different order for the GMM (i.e. more
gaussians) and/or different covariance matrix (full or diagonal). Do training and testing using
speech signals with variable duration. Add moderate level of noise. Compare the discrimination
ability between the classifiers.
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Write down very briefly your observations.
6. Give us your voice:
Record 10 signals using your voice (trying to avoid noisy recording conditions) and with 9 of
them construct your GMM. Then, with the remaining 10th signal you will test your speaker
identification system (i.e., include yourself in the pool of speakers considered before). You may
use 16000 Hz as sampling frequency during your recording and 16 bits resolution. You could
collaborate and enrich your data set using the recordings of your colleagues.
Does the identification system correctly finds the speakers? Report briefly.
Answers may be given in Greek or in English. Return the functions you wrote by yourself plus
the original (initial) Matlab file with the requested lines filled in.
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