VAE-based Domain Adaptation for Speaker Verification

TL;DR

This paper introduces a VAE-based domain adaptation method for speaker verification that effectively transforms speaker embeddings to match target domain conditions, improving performance in mismatched scenarios.

Contribution

The paper proposes a novel VAE-based approach for domain adaptation of speaker embeddings, enabling effective transformation with minimal target domain data.

Findings

VAE adaptation improves speaker verification accuracy in mismatched domains.

Transforming x-vectors into a regularized latent space facilitates domain adaptation.

The method requires only a small amount of target domain data for effective adaptation.

Abstract

Deep speaker embedding has achieved satisfactory performance in speaker verification. By enforcing the neural model to discriminate the speakers in the training set, deep speaker embedding (called `x-vectors`) can be derived from the hidden layers. Despite its good performance, the present embedding model is highly domain sensitive, which means that it often works well in domains whose acoustic condition matches that of the training data (in-domain), but degrades in mismatched domains (out-of-domain). In this paper, we present a domain adaptation approach based on Variational Auto-Encoder (VAE). This model transforms x-vectors to a regularized latent space; within this latent space, a small amount of data from the target domain is sufficient to accomplish the adaptation. Our experiments demonstrated that by this VAE-adaptation approach, speaker embeddings can be easily transformed to the target domain, leading to noticeable performance improvement.