Robust speaker identification in noisy environments using noise adaptive speaker models

The use of probabilistic mixture densities for text-independent speaker identification in a noisy telephone channel environment is investigated. Two techniques for noise compensation are considered. In the first approach, a background noise model is integrated directly into the model for speech. In the second approach, noise preprocessing techniques are used to compensate noisy observations before passing them along to the speaker identification on conversational utterances collected over long-distance telephone channels from ten speakers. The integrated noise model was noted for having a noise suppression characteristic that arises naturally from the statistical model, which is important if one wishes to avoid the ad hoc fine tuning of thresholds required in the noise processing approach implemented