These vectors build the data base for a speaker recognition test.
We experience, whether we can identify a speaker of that data base population by a 6 s speech test.
To test a text independant speaker recognition, the text sections used for the data base generation and those of the speech tests
were different (arbitrarily taken from a newspaper).
In the file generate_speaker_db.m characterizing vectors are computed for a series of speaker recordings.
For an unknown speaker the analogous vector has to be computed and compared with the data base.
We test the following type of a spectral characterization:
For the bandwidth 0 - 4000 Hz a vector is computed, whose components are amplitude means in 8.33 Hz steps.
Per speaker 6 time frames of 3 s duration are used, i.e. a 18 s speech recording as a wav-file with 44100 sampling rate.
For recognition an analogous vector of a test speaker is computed. The components of that vector in the data base
vector directions are computed. As "identification" is chosen that data base person, where we find the largest component.
This is done in recognize_speaker.m
For more details please read the text and comments in the m-files. The m-files are
m-file generate_speaker_db.m for the generation of a test data base with characterizing speaker spectra
m-file recognize_speaker.m for the recognition test
m-file plot_speaker_spectrum.m to plot the computed test speaker amplitude spectrum
Example of a single-sided amplitude speaker spectrum computed with generate_speaker_db.m
For new Matlab users:
You can open the m-files in the Matlab editor by double-clicking, and run them by
" Evaluate current cell", a menu point found in the top line of the Matlab window clicking "cell".
Tests
A test data base generated with 23 test speakers
List of speakers and corresponding numbers of their identities and test_track numbers
To experience how it works and compare the results with the speaker list, you can download
the test tracks with the filenames test_trackN.wav (each about 6 s) from the folder