This is a Speaker Recognition system with GUI. At first, it served as an SRT project for the course Signal Processing (2013Fall) in Tsinghua University. But we did find it pretty useful!

For more details of this project, please see:

• Our presentation slides
• Our complete report

• gcc >= 4.7
• SciPy
• scikit-learn
• scikits.talkbox
• pyssp
• PyQt
• PyAudio
• bob.

See here for instructions on bob core library installation.

See here for bob python bindings. If you install python bindings manually, you may need to install the following in order:

• bob.extension
• bob.blitz
• bob.core
• bob.sp
• bob.ap

We also have features implemented on our own, but just not so efficient as those C implementation in BOB.

(Optional) Run make -C src/gmm to compile our fast gmm implementation. (It is not used by default!)

Voice Activity Detection(VAD):

• Long-Term Spectral Divergence (LTSD)

Feature:

• Mel-Frequency Cepstral Coefficient (MFCC)
• Linear Predictive Coding (LPC)

Model:

• Gaussian Mixture Model (GMM)
• Universal Background Model (UBM)
• Continuous Restricted Boltzman Machine (CRBM)
• Joint Factor Analysis (JFA)

Our GUI not only has basic functionality for recording, enrollment, training and testing, but also has a visualization of real-time speaker recognition:

See our demo video (in Chinese) for more details.

usage: speaker-recognition.py [-h] -t TASK -i INPUT -m MODEL

Speaker Recognition Command Line Tool

optional arguments:
  -h, --help            show this help message and exit
  -t TASK, --task TASK  Task to do. Either "enroll" or "predict"
  -i INPUT, --input INPUT
                        Input Files(to predict) or Directories(to enroll)
  -m MODEL, --model MODEL
                        Model file to save(in enroll) or use(in predict)

Wav files in each input directory will be labeled as the basename of the directory.
Note that wildcard inputs should be *quoted*, and they will be sent to glob module.

Examples:
    Train:
    ./speaker-recognition.py -t enroll -i "/tmp/person* ./mary" -m model.out

    Predict:
    ./speaker-recognition.py -t predict -i "./*.wav" -m model.out