Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification

TL;DR

This paper introduces a two-stage iterative Procrustes algorithm to mitigate feature mismatch issues in VQ-based speaker verification, demonstrating improved performance across various conditions.

Contribution

The paper presents a novel two-stage iterative Procrustes matching algorithm specifically designed to address feature mismatch in VQ-based systems, enhancing speaker verification accuracy.

Findings

TIPM improves VQ-based speaker verification performance

Effective under both clean and noisy conditions

Reduces feature mismatch impact

Abstract

In the past decades, Vector Quantization (VQ) model has been very popular across different pattern recognition areas, especially for feature-based tasks. However, the classification or regression performance of VQ-based systems always confronts the feature mismatch problem, which will heavily affect the performance of them. In this paper, we propose a two-stage iterative Procrustes algorithm (TIPM) to address the feature mismatch problem for VQ-based applications. At the first stage, the algorithm will remove mismatched feature vector pairs for a pair of input feature sets. Then, the second stage will collect those correct matched feature pairs that were discarded during the first stage. To evaluate the effectiveness of the proposed TIPM algorithm, speaker verification is used as the case study in this paper. The experiments were conducted on the TIMIT database and the results show that TIPM can improve VQ-based speaker verification performance clean condition and all noisy conditions.