Weakly Supervised Training of Speaker Identification Models

Martin Karu, Tanel Alumäe


We propose an approach for training speaker identification models in a weakly supervised manner. We concentrate on the setting where the training data consists of a set of audio recordings and the speaker annotation is provided only at the recording level. The method uses speaker diarization to find unique speakers in each recording, and i-vectors to project the speech of each speaker to a fixed-dimensional vector. A neural network is then trained to map i-vectors to speakers, using a special objective function that allows to optimize the model using recording-level speaker labels. We report experiments on two different real-world datasets. On the VoxCeleb dataset, the method provides 94.6% accuracy on a closed set speaker identification task, surpassing the baseline performance by a large margin. On an Estonian broadcast news dataset, the method provides 66% time-weighted speaker identification recall at 93% precision.


 DOI: 10.21437/Odyssey.2018-4

Cite as: Karu, M., Alumäe, T. (2018) Weakly Supervised Training of Speaker Identification Models . Proc. Odyssey 2018 The Speaker and Language Recognition Workshop, 24-30, DOI: 10.21437/Odyssey.2018-4.


@inproceedings{Karu2018,
  author={Martin Karu and Tanel Alumäe},
  title={Weakly Supervised Training of Speaker Identification Models	},
  year=2018,
  booktitle={Proc. Odyssey 2018 The Speaker and Language Recognition Workshop},
  pages={24--30},
  doi={10.21437/Odyssey.2018-4},
  url={http://dx.doi.org/10.21437/Odyssey.2018-4}
}