Short- and Long-Term Speech Features for Hybrid HMM-i-Vector based Speaker Diarization System

Abraham Woubie Zewoudie, Jordi Luque, Javier Hernando


i-vectors have been successfully applied over the last years in speaker recognition tasks. This work aims at assessing the suitability of i-vector modeling within the frame of speaker diarization task. In such context, a weighted cosine-distance between two different sets of i-vectors is proposed for speaker clustering. Speech clusters generated by Viterbi segmentation are first modeled by two different i-vectors. Whilst the first i-vector represents the distribution of the commonly used short-term Mel Frequency Cepstral Coefficients, the second one depicts a selection of voice quality and prosodic features. In order to combine both short- and long-term speech statistics, the cosine-distance scores of those two i-vectors are linearly weighted to obtain a unique similarity score. The final fused score is then used as speaker clustering distance. Our experimental results on two different evaluation sets of the Augmented Multi-party Interaction corpus show the suitability of combining both sources of information within the i-vector space. Our experimental results show that the use of i-vector based clustering technique provide a significant improvement, in terms of diarization error rate, than those based on Gaussian Mixture Modeling technique. Furthermore, this work also reports a significant speaker error reduction by augmenting short-term based i-vector clustering with a second i-vector estimated from voice quality and prosody related speech features.


DOI: 10.21437/Odyssey.2016-58

Cite as

Zewoudie, A.W., Luque, J., Hernando, J. (2016) Short- and Long-Term Speech Features for Hybrid HMM-i-Vector based Speaker Diarization System. Proc. Odyssey 2016, 400-406.

Bibtex
@inproceedings{Zewoudie+2016,
author={Abraham Woubie Zewoudie and Jordi Luque and Javier Hernando},
title={Short- and Long-Term Speech Features for Hybrid HMM-i-Vector based Speaker Diarization System},
year=2016,
booktitle={Odyssey 2016},
doi={10.21437/Odyssey.2016-58},
url={http://dx.doi.org/10.21437/Odyssey.2016-58},
pages={400--406}
}