Duration Features in Prosodic Classification: Why Normalization Comes Second, and What They Really Encode

For the classification of boundaries and accents in German and English spontaneous speech in the VERBMOBIL project (speech to speech translation system), we use a large prosodic feature vector; duration features represent the most important feature class. They are computed in three different ways: (1) The word duration is normalized with respect to the ‘expected’ word duration: DURNORM; (2) Duration is normalized as for the number of syllables in the word: DURSYLL; (3) The absolute duration value DURABS of a word is taken. Normally, we use all these feature classes simultaneously. In the present paper, we have a look at the impact of each of these duration classes separately. In addition, we use partof-speech (POS) information as a further knowledge source. It turns out that throughout, the best feature class, if used alone, is DURABS, followed by DURSYLL, and third comes DURNORM. Best results are achieved by using all feature classes together. With POS information, better results can be achieved than without. This effect is larger for accent classification than for boundary classification, and much larger in combination with DURNORM than in combination with DURSYLL or DURABS. These results indicate that especially DURABS does not only encode prosodic but to a large extent syntactic POS information as well: content words are normally more prone to be accentuated than function words, and at the same time, they tend to be longer. This information is of course lost if duration is normalized, as is the case for DURSYLL and DURNORM.