An approach to speech recognition using syllabic decision units

Abstract

Described is an automatic speech recognition system which uses parts of syllables as decision units, i.e. the syllable nuclei, the initial consonant clusters preceding the nuclei and the final consonant clusters following the nuclei. This segmentation includes monosyllabic words as well as polysyllabic words. The segmentation into these units is achieved by evaluating a modified loudness function which is generated by a special loudness analyzer equipment. Application of a time normalization procedure to the time-varying patterns of the consonant clusters yields feature vectors of a constant length. Classification experiments using a test set of 3000 utterances of initial and final consonant clusters have been performed. Although the segmentation of the proposed units is much easier than that of single phonems, recognition scores are equivalent. An automatic speech recognition system has been constructed using a vocabulary consisting of the names of 230 German cities.