Improving the Effectiveness of Speaker Verification Domain Adaptation With Inadequate In-Domain Data

Published

August 24, 2017

Author(s)

Bengt Borgstrom, Elliot Singer, Douglas Reynolds, Omid Sadjadi, Gregory A. Sanders

Abstract

This paper addresses speaker verification domain adaptation with inadequate in-domain data. Specifically, we explore he cases where in-domain data sets do not include speaker labels, contain speakers with few samples, or contain speakers with low channel diversity. Existing domain adaptation methods are reviewed, and their shortcomings are discussed. We derive an unsupervised version of fully Bayesian adaptation which reduces the reliance on rich in-domain data. When applied to domain adaptation with inadequate in-domain data, the proposed approach yields competitive results when the samples per speaker are reduced, and outperforms existing supervised methods when the channel diversity is low, even without requiring speaker labels. These results are validated on the SRE16, which uses a highly inadequate in-domain data set.

Proceedings Title

Interspeech 2017

Conference Dates

August 20-24, 2017

Conference Location

Stockholm, SE

Pub Type

Conferences

Keywords

speaker verification, unsupervised domain adaptation, Bayesian adaptation

Citation

Borgstrom, B. , Singer, E. , Reynolds, D. , Sadjadi, O. and Sanders, G. (2017), Improving the Effectiveness of Speaker Verification Domain Adaptation With Inadequate In-Domain Data, Interspeech 2017, Stockholm, SE (Accessed July 27, 2024)