Open Phd/postdoc positions at LIMSI combining machine learning, NLP, speech processing, and computer vision.

Neural building blocks for speaker diarization

$ conda create --name pyannote python=3.6 anaconda
$ source activate pyannote
$ conda install -c conda-forge yaafe
$ conda install cmake
$ pip install -U pip setuptools
$ pip install --process-dependency-links pyannote.audio

If you use pyannote.audio in your research, please use the following citations.

• Speech activity and speaker change detection

  @inproceedings{Yin2017,
    Author = {Ruiqing Yin and Herv\'e Bredin and Claude Barras},
    Title = {{Speaker Change Detection in Broadcast TV using Bidirectional Long Short-Term Memory Networks}},
    Booktitle = {{18th Annual Conference of the International Speech Communication Association, Interspeech 2017}},
    Year = {2017},
    Month = {August},
    Address = {Stockholm, Sweden},
    Url = {https://github.com/yinruiqing/change_detection}
  }

• Speaker embedding

  @inproceedings{Bredin2017,
      author = {Herv\'{e} Bredin},
      title = {{TristouNet: Triplet Loss for Speaker Turn Embedding}},
      booktitle = {42nd IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017},
      year = {2017},
      url = {http://arxiv.org/abs/1609.04301},
  }

• Speaker diarization pipeline

  @inproceedings{Yin2018,
    Author = {Ruiqing Yin and Herv\'e Bredin and Claude Barras},
    Title = {{Neural Speech Turn Segmentation and Affinity Propagation for Speaker Diarization}},
    Booktitle = {{19th Annual Conference of the International Speech Communication Association, Interspeech 2018}},
    Year = {2018},
    Month = {September},
    Address = {Hyderabad, India},
  }

• Feature extraction
• LSTM-based speech activity detection
• LSTM-based speaker change detection
• TristouNet neural speech turn embedding
• Speaker diarization pipeline

The API is unfortunately not documented yet.