Designing an Effective Metric Learning Pipeline for Speaker Diarization
Abstract
State-of-the-art speaker diarization systems utilize knowledge from external data, in the form of a pre-trained distance metric, to effectively determine relative speaker identities to unseen data. However, much of recent focus has been on choosing the appropriate feature extractor, ranging from pre-trained vectors to representations learned via different sequence modeling architectures (e.g. 1D-CNNs, LSTMs, attention models), while adopting off-the-shelf metric learning solutions. In this paper, we argue that, regardless of the feature extractor, it is crucial to carefully design a metric learning pipeline, namely the loss function, the sampling strategy and the discrimnative margin parameter, for building robust diarization systems. Furthermore, we propose to adopt a fine-grained validation process to obtain a comprehensive evaluation of the generalization power of metric learning pipelines. To this end, we measure diarization performance across different language speakers, and variations in the number of speakers in a recording. Using empirical studies, we provide interesting insights into the effectiveness of different design choices and make recommendations.
Cite
@article{arxiv.1811.00183,
title = {Designing an Effective Metric Learning Pipeline for Speaker Diarization},
author = {Vivek Sivaraman Narayanaswamy and Jayaraman J. Thiagarajan and Huan Song and Andreas Spanias},
journal= {arXiv preprint arXiv:1811.00183},
year = {2018}
}