Acoustic-phonetic transformations for improved speaker-independent isolated word recognition

The authors present a method for improving HMM (hidden Markov model) phonetic discrimination capability which is a linear discriminant transform of acoustic features to a continuous-valued feature space such that phonetic distinctions correlate closely with Euclidean distance in the transformed feature space. Experimental testing with a 30-word single syllable highly confusable vocabulary showed that the acoustic-phonetic transform could be used to reduce word error rates approximately 25%. In general, results based on the LDA2 transform, i.e., linear discriminant analysis with whitening of the within-class covariance matrices, are superior to those obtained with LDA1, linear discriminant analysis without whitening. Recognition results also improve if a block transform of several frames per block is used rather than a transform based on one frame per block