Speaker adaptive Phoneme recognition based on feature mapping from spectral domain to probabilistic domain

A new feature parameter space for speech recognition called PRPG (Probability Ratios between Phoneme Group pairs) has been proposed and speaker adaptive phoneme recognition has been performed. In the coordinate system proposed here, the area with the same information for speech recognition is compressed into one point. The mapping function from spectral coordinate system to proposed one is realized using a neural network. The code-vectors designed on this coordinate system are assured to be informationtheoretically more efficient than that of spectral coordinate system. Moreover, by the definition of the coordinate system, the meaning of axes are equivalent among different speakers, so the speaker adaptation can be easily performed without trajectory mapping. The experimental results show that the 40% of errors are reduced by the coordinate conversion in the speaker-dependent tasks. The scores of the speakeradaptive tasks in the proposed feature domain are always superior to those of the speaker-dependent tasks in the spectral domain.