Automatic recognition of syllabic speech segments using spectral and temporal features

An automatic speech recognition system is presented which starts from a demisyllable segmentation of the speech signal. Recognition of these segments is based on a set of spectral and temporal acoustic features which are automatically extracted from LPC-spectra and assembled in one feature vector for each demisyllable. The 24 components of this vector describe formants, formant loci, formant transitions, formant-like "links" for characterization of nasals, liquids or glides, the spectral distribution of fricative noise or bursts (turbulences), and duration of pauses. Preliminary recognition experiments were carried out with feature vectors extracted from a set of 360 German initial demisyllables which represent 45 consonant clusters combined with 8 vowels. When compared with template matching methods, the feature representations yield a drastic reduction in the number of components needed to represent each segment.