  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.
