Abstract
Speech rhythm in terms of durational variability of different levels of phonetic intervals can vary between speakers. The present article examines the role of syl- labic intensity characteristics in rhythmic variability. Mean and peak intensity variability across syllables (stdevM, varcoM, stdevP, varcoP, rPVIm, nPVIm, rPVIp, nPVIp; henceforth: intensity measures) were investigated as a function of speaker in a database where within-speaker variability was strong (BonnTempo) and another database designed to examine between-speaker rhythmic variability (TEVOID). It was found that the intensity measures varied significantly between speakers in both databases. Semiautomatic speaker recognition based on duration measures (%V, ∆V(ln), ∆C(ln), ∆Peak(ln), ∆Syll(ln) and nPVISyll) and intensity measures using multinomial logistic regression and feedforward neural networks was carried out for the two databases. Results showed that intensity measures contained stronger speaker specific information compared to measures based on durational variability of phonetic intervals. In addition, effects of the recognition algorithms (speaker rec- ognition using multinomial logistic regression was significantly better than neural networks for BonnTempo) and data normalisation procedures (z-score normalised data was significantly better than non-normalised data in TEVOID) were discov- ered. This means that syllable intensity characteristics play an important role in between-speaker rhythmic differences and possibly in speech rhythm variability in general.
He, Lei