# Quality Measures for Speaker Verification with Short Utterances

## TL;DR

This paper introduces new quality measures based on zero-order statistics to enhance the combination of speaker verification systems, significantly improving performance with short utterances.

## Contribution

It proposes novel quality measures derived from zero-order statistics for better system combination in short-utterance speaker verification.

## Key findings

- Significant performance improvement on NIST SRE datasets.
- Outperforms existing duration-based quality measures.
- Effective use of quality measures as side information in system fusion.

## Abstract

The performances of the automatic speaker verification (ASV) systems degrade due to the reduction in the amount of speech used for enrollment and verification. Combining multiple systems based on different features and classifiers considerably reduces speaker verification error rate with short utterances. This work attempts to incorporate supplementary information during the system combination process. We use quality of the estimated model parameters as supplementary information. We introduce a class of novel quality measures formulated using the zero-order sufficient statistics used during the i-vector extraction process. We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model-universal background model (GMM-UBM) and i-vector. The proposed methods demonstrate considerable improvement in speaker recognition performance on NIST SRE corpora, especially in short duration conditions. We have also observed improvement over existing systems based on different duration-based quality measures.