Final Report

Project Links
Seth's Homepage  
Speech ID Main
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The goal of this project was to combine whatever code could be found, and write what couldn’t to create a speaker verification program that ran on the average Windows computer using the soundcard for input. Because of these goals, the project was written in C++ with the Visual C++ compiler. The author was able to write a program that would train and test data. The results from comparing the probability of the test data and the training data from the model would ideally show how likely the test data was from the same person as the training data. This system seemed to work best with background noise.

Speaker Verification vs. Speaker ID

This project is speaker verification. Speaker verification asks the question, “Is this person really who they say they are?” The flow of events runs like this, the person tells the computer who they are, the computer asks for a password, the person says the password, and the computer tells them if they passed the test.

Speaker ID asks, “Who of the people I know is this most likely to be?” In this case, the computer has a database of models for different people. When the test case comes in, the computer compares it to all the models, and chooses the most likely one. This is very similar to speaker dependant speech recognition, only the computer is distinguishing between people instead of words.

This project is speaker ID, because the author (me) would like to have his computer identify the people coming into his apartment. The goal is to have the computer also be the answering machine. That way, when I walk in the door, it asks me who I am, and then tells me if I have any messages and who left them.

Features Used

The main two sets of features used in speaker verification (ID is the same) are Mel-Cepstrum (MFCC), and delta Cepstrum (DFCC). The Glottal flow derivative (GFD) also works well for speaker verification, but the author has studied MFCC much more and MFCCs are easier to derive, so that is what he used. The C++ functions he found could calculate the MFCC, DFCC (which is the first derivative of the MFCCs), and the second derivatives of the MFCCs.

Gaussian Mixture Modeling

Gaussian mixture modeling can be thought of as using multiple Gaussians to fit a non-Gaussian data set. This is not a temporal modeling scheme, unlike the Hidden Markov Models. The parameters have to be found iteratively, usually through an expectation maximization process.

Software Found 

Here is a list of the software examples I looked at:
Jialong He 's library
CSLU Toolkit
X.  H. Li 's
HTK Speech Kit
Microsoft Speech API
E-Med Innovations Inc. Article with code
Paul Cheffers Multithreaded wave interface

Software Used

The two sets of software I found that I could coerce into doing what I wanted were X. H. Li’s and E-Med Innovations from Codeproject .

What I did

I did some rework in the E-Med Innovations software to make the recording energy sensitive. The goal was to cut out most of the silence in the recording. Then I added a method to X. H. Li’s CGMM class to test data against the GMM. Then I wrote the software to connect everything together. This means I spent most of my time trying to figure out how other people’s software worked. Once I had it figured out, I had to figure out how to get the data I needed out of it. This became a problem in some of the packages I tried. Some were pretty easy to use, but I needed to extract the wave data and plug it into my feature calculation classes. Unfortunately, some programs wouldn’t allow this. Then, I figured out how to get the data out, but it was a void* to shorts and I needed a float*. So Seth learned a bit more C++. Finally, everything was just getting going, and the earlier stuff began to break down. So I looked everything over carefully, found where the problems were, and now it runs.

What the program does

Here is an example of what the GUI looks like:

Screen Shot

First, pick the device to use for sound. Second, click Record Training to record a training sample. After you stop recording it will tell you it successfully recorded it, and then extract the features. This program only uses MFCCs right now. All data is stored in a data subdirectory from the working directory of the program. You can play the last training data to see what it sounds like (sorry can’t erase it from in here yet). Once you have a suitable number of training samples, click Train Model to train a GMM. The GMM is trained from all the train*.mfc files in the params.lis file. You can edit this to change which ones to use. The Test Training Data button shows the average likelihood of all the training samples. It also shows the individual likelihoods in the window below. Now that there is a model, click on Record Test to record a test sample, then Test to see what the probability of the test sample is.

What the results are

The  likelihoods are the sum of all the likelihoods for the each 100 ms window of speech from the model. Because these are log-likelihoods they are negative and the more negative numbers are less likely. I tested it with myself as both the test and training, and the results were inconclusive. However, if you train it and then test it on silence (background noise) the likelihood is very good. It makes me a little suspicious. Comparing it with other speakers, the differentiation is not great. Right now it is running on 10 MFCCs, and a 5th order GMM. I trained it with 10 training samples of ~1 second each.


It may have been more worthwhile and faster just to write all the functions myself from scratch, particularly the feature extraction and model functions. The wave I/O would have been a bit more challenging. But I did succeed in getting everyone’s functions working together pretty happily. I was surprised at two things: how few functions are on the web to do exactly what I wanted, and how difficult it is to use someone else’s code to accomplish what I wanted. If I did it again, I would probably have done a Matlab project because I know it better and it gives results in a much better presentation.

Future Work

More testing to determine exactly what is going on is needed. This was curtailed to get the project turned in. Next, I would add a multimodel system to do speaker ID, and make the recording system better by checking the energy level and automatically cutting off when there has been silence for sometime. Also, I would make it automatically detect the background noise level and possibly model it to subtract it out. I was reading in [2] and they say that even recording in the same location with the same people a couple of days apart can give different results.

Works that inspired this project

All the webpages told a little more about what could be done, and what was being done.
[1] Quatieri, T. Discrete-Time Speech Signal Processing. Prentice Hall, NJ. 2002
[2] Strom, P, et al. Speaker Recognition for User Identification and Verification. Project Course in Signal Processing and Digital Communication at the Royal Institute of Technology, Sweden. 2001.