Evaluation and calibration of Lombard effects in speaker verification

Abstract

The Lombard effect is the involuntary tendency of speakers to increase their vocal effort in noisy environments in order to maintain intelligible communication. This study assesses the impact of the Lombard effect on the performance of a current speaker verification system. Lombard speech produced in the presence of several noise types and noise levels is drawn from the UT-Scope corpus. The performance of an i-vector PLDA (Probabilistic Linear Discriminant Analysis) system is observed to degrade significantly with Lombard speech. The resulting error rates are found to be dependent on the noise type and noise level. A score calibration scheme based on Quality Measure Functions (QMFs) is adopted, allowing noise information to be incorporated into calibration. This approach leads to a reduction in discrimination error relative to conventional calibration.