Psychoacoustic Model Compensation for Robust Speaker Verification in Environmental Noise

Abstract

We investigate the problem of speaker verification in noisy conditions in this paper. Our work is motivated by the fact that environmental noise severely degrades the performance of speaker verification systems. We present a model compensation scheme based on the psychoacoustic principles that adapts the model parameters in order to reduce the training and verification mismatch. To deal with scenarios where accurate noise estimation is difficult, a modified multiconditioning scheme is proposed. The new algorithm was tested on two speech databases. The first database is the TIMIT database corrupted with white and pink noise and the noise estimation is fairly easy in this case. The second database is the MIT Mobile Device Speaker Verification Corpus (MITMDSVC) containing realistic noisy speech data which makes the noise estimation difficult. The proposed scheme achieves significant performance gain over the baseline system in both cases.