A Comparative Study of Methods for Handheld Speaker Verification in Realistic Noisy Conditions

A number of methods have been studied and compared for their robustness for speaker verification using noisy speech samples. The methods include Wiener filtering, noise compensation, missing-feature technique, universal compensation, and their combinations. Strategies for combining different techniques are investigated, as a means of further improving noise robustness. A handheld-device database, collected in realistic conditions with noise corruption and transducer mismatch between training and testing, is used in the study. The various techniques and proposed combinations are compared within the same feature and model framework for characterizing the speakers. The experimental results indicate that: 1) usual noise filtering and noise compensation provided very limited robustness to noise corruption, and 2) the proposed technique combinations offered significantly improved noise robustness