About

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

Dependencies

• SciPy
• scikit-learn
• scikits.talkbox
• pyssp
• PyQt
• PyAudio
• (Optional)bob.

Installation / Compilation

(Optional) Bob:

See here for instructions on bob core library installation.

Bob python bindings are available on PyPI. You may need to install bob packages in the following order:

• bob.extension
• bob.blitz (require blitz++)
• bob.core
• bob.sp
• bob.ap

Note: We also have MFCC feature implemented on our own, which will be used as a fallback when bob is unavailable. But it's not so efficient as the C implementation in bob.

(Optional) GMM

Run make -C src/gmm to compile our fast gmm implementation. Require gcc >= 4.7.

It will be used as default, if successfully compiled.

Algorithms Used

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)

GUI Demo

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

You should understand that real-time speaker recognition is extremely hard, because we only use corpus of about 1 second length to identify the speaker. Therefore the real-time system doesn't work very perfect. You can See our demo video (in Chinese).

Command Line Tools

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