Missing-Feature Method for Speaker Recognition in Band-Restricted Conditions

In this study, the missing-feature method is considered to address band-limited speech for speaker recognition. In an effort to mitigate possible degradation due to the general speaker independent model, a two-step reconstruction scheme is developed, where speaker class independent/dependent models are used separately. An advanced marginalization in the cepstral domain is proposed employing a high order extension method in order to address loss of model accuracy in the conventional method due to cepstrum truncation. To detect the cut-off regions from incoming speech, a blind mask estimation scheme is employed which uses a synthesized band-limited speech model. Experimental results on band-limited conditions indicate that our two-step reconstruction scheme with missing-feature processing is effective in improving in-set/out-of-set speaker recognition performance for band-limited speech, particularly in severely band-restricted conditions (i.e., 4.72% EER improvement in 2, 3, and 4kHz band-limited conditions over a conventional data-driven method). The improvement of the proposed marginalization method proves its effectiveness for acoustic model conversion by employing high order extension, showing 0.57% EER improvement over conventional marginalization.