Experiments on The Use of Demisyllables For Automatic Speech Recognition
Abstract
The paper describes methods for an explicit segmentation of the speech signal into demisyllable segments by evaluating the output of a loudness model. Syllable nuclei are indicated by maxima of a smoothed loudness function. Consonant clusters and vowels are introduced as decision units in order to reduce the inventory of classes. Two methods for classification of consonant clusters are compared: template matching and a feature extraction approach based on acoustic cues. Sentence recognition operates on phonetic word models adapted to the demisyllable structure.
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