@inproceedings{ahmed-etal-2017-unsupervised,
    title = "An Unsupervised Speaker Clustering Technique based on {SOM} and {I}-vectors for Speech Recognition Systems",
    author = "Ahmed, Hany  and
      Elaraby, Mohamed  and
      M. Mousa, Abdullah  and
      Elhosiny, Mostafa  and
      Abdou, Sherif  and
      Rashwan, Mohsen",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-1310",
    doi = "10.18653/v1/W17-1310",
    pages = "79--83",
    abstract = "In this paper, we introduce an enhancement for speech recognition systems using an unsupervised speaker clustering technique. The proposed technique is mainly based on I-vectors and Self-Organizing Map Neural Network(SOM).The input to the proposed algorithm is a set of speech utterances. For each utterance, we extract 100-dimensional I-vector and then SOM is used to group the utterances to different speakers. In our experiments, we compared our technique with Normalized Cross Likelihood ratio Clustering (NCLR). Results show that the proposed technique reduces the speaker error rate in comparison with NCLR. Finally, we have experimented the effect of speaker clustering on Speaker Adaptive Training (SAT) in a speech recognition system implemented to test the performance of the proposed technique. It was noted that the proposed technique reduced the WER over clustering speakers with NCLR.",
}