Experiments on The Use of Demisyllables For Automatic Speech Recognition
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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