@InProceedings{ahmed-EtAl:2017:W17-13,
  author    = {Ahmed, Hany  and  Elaraby, Mohamed  and  M. Mousa, Abdullah  and  Elhosiny, Mostafa  and  Abdou, Sherif  and  Rashwan, Mohsen},
  title     = {An Unsupervised Speaker Clustering Technique based on SOM and I-vectors for Speech Recognition Systems},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-1310}
}