Unsupervised Voice Type Discrimination Score Adaptation Using X-Vector Clusters

Mark Lindsey; Tyler Vuong; Richard M. Stern

ICASSP 2023accepted0 citations

Unsupervised Voice Type Discrimination Score Adaptation Using X-Vector Clusters

Mark Lindsey, Tyler Vuong, Richard M. Stern

Abstract

Voice type discrimination (VTD) is the task of automatically detecting speech produced in the same room as a recording device ("live speech") among other speech and non-speech noises, such as traffic noises or radio broadcasts ("distractor audio"). Existing work has described methods for performing the VTD task. This paper presents a method for adapting the output of these existing methods in an unsupervised manner via x-vector clustering and correlation. This adaptation method can be applied to the output of any VTD algorithm, requires no additional training data, and has been shown to yield a relative decrease in decision cost function (DCF) score of up to 47% on a standardized database collected for the task.

BibTeX

@inproceedings{icassp2023_unsupervisedvoic,
  title = {Unsupervised Voice Type Discrimination Score Adaptation Using X-Vector Clusters},
  author = {Mark Lindsey and Tyler Vuong and Richard M. Stern},
  booktitle = {ICASSP 2023},
  year = {2023}
}