The efficiency of demisyllable segmentation in the recognition of spoken words

The efficiency of syllabic segmentation and recognition is demonstrated in an experiment using three different word recognition systems and a vocabulary of 1000 words. In each system the preprocessing is carried out by a special loudness analyzer which yields 22 specific loudness functions. The first system avoids any segmentation and the total word pattern is time normalized to a constant length. In the second system syllable nuclei are detected and used as segment boundaries; the segments are time normalized and the resulting word pattern classified. The third system classifies each demisyllable using vowels and consonant clusters as decision units. For small vocabularies the first system gives the best performance. For more than 500 words the performance of the second system with syllabic segmentation surpasses that of the first system. For this vocabulary size however, the demisyllable recognition performs best and is significantly advantageous for a vocabulary consisting of 1000 words.