Adaptive training method for pattern recognition

摘要

Training data is LPC analyzed to obtain a feature parameter vector sequence, which is subjected to Viterbi segmentation using reference phoneme models to separate phonemes. Each piece of phoneme data is used to estimate a mean vector of the corresponding reference phoneme model by a maximum a posteriori estimation method. The adapted phoneme model and the corresponding reference phoneme model are used to estimate a mean vector for an unadapted phoneme model through interpolation by a vector field smoothing method. Alternatively, the mean vector of the adapted phoneme model is further smoothed by the vector field smoothing method. By this, an adapted model is obtained which has, as its parameters, the mean vector obtained for each phoneme and other corresponding parameters. $$