Missing feature theory with soft spectral subtraction for speaker verification

This paper considers the problem of training/testing mismatch in the context of speaker verification and, in particular, explores the application of missing feature theory in the case of additive white Gaussian noise corruption in testing. Missing feature theory allows for corrupted features to be removed from scoring, the initial step of which is the detection of these features. One method of detection, employing spectral subtraction, is studied in a controlled manner and it is shown that with missing feature compensation the resulting verification performance is improved as long as a minimum number of features remain. Finally, a blending of “soft” spectral subtraction for noise mitigation and missing feature compensation is presented. The resulting performance improves on the constituent techniques alone, reducing the equal error rate by about 15% over an SNR range of 5 25 dB.