Speaker Recognition in Adverse Conditions

Recognizing speakers from their voices is a challenging area of research with several practical applications. Presently speaker verification (SV) systems achieve a high level of accuracy under ideal conditions such as, when there is ample data to build speaker models and when speaker verification is performed in the presence of little or no interference. In general, these systems assume that the features extracted from the data follow a particular parametric probability density function (pdf), i.e., Gaussian or a mixture of Gaussians; where a form of the pdf is imposed on the speech data rather than determining the underlying structure of the pdf. In practical conditions, like in an aircraft cockpit where most of the verbal communication is in the form of short commands, it is almost impossible to ascertain that the assumptions made about the structure of the pdf are correct, and wrong assumptions could lead to significant reduction in performance of the SV system. In this research, non-parametric strategies, to statistically model speakers are developed and evaluated. Non-parametric density estimation methods are generally known to be superior when limited data is available for model building and SV. Experimental evaluation has shown that the non-parametric system yielded a 70% accuracy level in speaker verification with only 0.5 seconds of data and under the influence of noise with signal-to-noise ratio of 5dB. This result corresponds to a 20% decrease in error when compared to the parametric system.