The Lombard Effect's Influence on Automatic Speaker Verification Systems and Methods for its Compensation

Speaker identification/verification applications have progressed significantly during the last few years. Performance levels of between 70% -99% success in speaker recognition systems are normal, depending on the type of application and quality of the signal. Several techniques for robust speaker recognition have been developed. Until now, however, the problem posed by variations in speech characteristics due to acoustical noise has not been thoroughly investigated in the context of speaker recognition. The change a noisy acoustic environment can produce in speech signal parameters is known as the "Lombard effect." In this paper the Lombard effect's influence on speaker verification system performance is investigated and several compensation methods are proposed. The verification system is based on a 24 Gaussian mixture model (GMM) and speech feature orders of 12 to 60. It was found that, based on the mean Equal Error Rate (EER), verification performance deteriorated by 10.1% (from 3.8% to 13.9%) relative to speech verification in a normal environment due to the Lombard Effect. Two types of Lombard Effect compensation methods are proposed. The first is based on robust speech features that are resistant to the Lombard effect. The second is based on studying how the Lombard effect changes speech feature and then transforming the Lombard affected speech back to normal speech. The proposed methods significantly reduce speaker verification system error rates. An improvement in the EER of up to 5.4 % (from 13.5% to 8.5%) was achieved.