Randomization Effect on Iterative-Based Speaker Diarization System for Telephone Conversations

The primary objective of speaker diarization system is to designate speech segments to one of K speakers in the conversation. We use a hidden-distortion-model (HDM)-based system. HDM allows using different emission models as speaker models. We investigate the effect of randomization in two different levels. One level is stochastic training versus deterministic training and the other, random model initialization versus preserving initialization from the previous iteration. The emission models were codebooks (CBs) trained using K-means algorithm, both, batch and stochastic versions, as well as a self-organizing map (SOM) in its stochastic version. The evaluation performed on 108 telephone conversations from the LDC CallHome corpus. We will show that randomizing is always outperforming the deterministic training. Stochastic training demonstrated relative improvement of 3.5%. Random initialization achieved relative improvement of 7.28% comparing to preservation of initialization from the previous iteration.