Compensation with prior knowledge
All methods analyzed and contrasted in this chapter have the unique attribute of exploiting prior knowledge about distortion in the training stage, in addition to training an HMM. They then use such prior knowledge as a guide to either remove noise or adapt models in the testing or deployment stage. Most methods which use prior knowledge about acoustic distortions as discussed in this chapter learn the nonlinear mapping functions between the clean and noisy speech features when they are available in the training phase as a pair of stereo data. By modeling the differences between the features or models of the stereo data, a distortion model can be learned accurately in training and subsequently ...
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