Speaker Diarization based on Bayesian HMM with Eigenvoice Priors

This python code implements speaker diarization algorithm described in:

Diez Mireia, Burget Lukáš and Matějka Pavel. Speaker Diarization based on Bayesian HMM with Eigenvoice Priors. In: Proceedings of Odyssey 2018. Les Sables d´Olonne: International Speech Communication Association, 2018, pp. 147-154. ISSN 2312-2846. available at: http://www.fit.vutbr.cz/research/groups/speech/publi/2018/diez_odyssey2018_63.pdf


Nowadays, most speaker diarization methods address the task in two steps: segmentation of the input conversation into (preferably) speaker homogeneous segments, and clustering. Generally, different models and techniques are used for the two steps. In this paper we present a very elegant approach where a straightforward and efficient Variational Bayes (VB) inference in a single probabilistic model addresses the complete SD problem. Our model is a Bayesian Hidden Markov Model, in which states represent speaker specific distributions and transitions between states represent speaker turns. As in the i-vector or JFA models, speaker distributions are modeled by GMMs with parameters constrained by eigenvoice priors. This allows to robustly estimate the speaker models from very short speech segments. The model, which was released as open source code and has already been used by several labs, is fully described for the first time in this paper. We present results and the system is compared and combined with other state-of-the-art approaches. The model provides the best results reported so far on the CALLHOME dataset.

Download: http://www.fit.vutbr.cz/~burget/VB_diarization.zip (attention, the file has 35 MB as it contains example data).