ISCA Archive Odyssey 2014
ISCA Archive Odyssey 2014

Telephone Conversation Speaker Diarization Using Mealy-HMMs

Jean-François Bonastre, Itshak Lapidot, Samy Bengio

When Hidden Markov Models (HMMs) were first introduced, two competing representation models were proposed, the Moore model, with separate emission and transition distributions, which is commonly used in speech technologies, and the Mealy model, with a single emission-transition distribution. Since then the literature has mostly focused on the Moore model. In this paper, we would like to show the use of Mealy-HMMs for telephone conversation speaker diarization task. We present the Viterbi training and decoding for Mealy-HMMs and show that it yields similar performance compared to Moore-HMMs with a fewer number of parameters.

doi: 10.21437/Odyssey.2014-27

Cite as: Bonastre, J.-F., Lapidot, I., Bengio, S. (2014) Telephone Conversation Speaker Diarization Using Mealy-HMMs. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 173-178, doi: 10.21437/Odyssey.2014-27

  author={Jean-François Bonastre and Itshak Lapidot and Samy Bengio},
  title={{Telephone Conversation Speaker Diarization Using Mealy-HMMs}},
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},