Analysis and Calibration of Lombard Effect and Whisper for Speaker Recognition

Variations in vocal effort can create challenges for speaker recognition systems that are optimized for use with neutral speech. The Lombard effect and whisper are two commonly-occurring forms of vocal effort variation that result in non-neutral speech, the first due to noise exposure and the second due to intentional adjustment on the part of the speaker. In this article, a comparative evaluation of speaker recognition performance in non-neutral conditions is presented using multiple Lombard effect and whisper corpora. The detrimental impact of these vocal effort variations on discrimination and calibration performance on global, per-corpus, and per-speaker levels is explored using conventional error metrics, along with visual representations of the model and score spaces. A non-neutral speech detector is subsequently introduced and used to inform score calibration in several ways. Two calibration approaches are proposed and shown to reduce error to the same level as an optimal calibration approach that relies on ground-truth vocal effort information. This article contributes a generalizable methodology towards detecting vocal effort variation and using this knowledge to inform and advance speaker recognition system behavior.