Jacques C. Koreman

Do Rhythm Measures Reflect Perceived Rhythm

In a production study, Bulgarian, English and German verses with regular poetic metrical metres of different types and elicited prose utterances with varied accentual patterns are produced in textual and iterative (dada) form and measured at syllable level according to the pairwise variability index (PVI) principle. Systematic differences in PVI values show that the measure is sensitive to metrical differences. But variations for utterances with the same metrical structure and comparable measures for accentually different utterances show the measure to be insensitive to the temporal distribution of accents. A perceptual experiment with Bulgarian, English and German subjects confirms the hypothesis that the perceived strength of rhythmicity in a line of verse is determined not only by its temporal structure, but also by other acoustic properties, most clearly by F₀ change within the metrical foot.

How linguistic and probabilistic properties of a word affect the realization of its final /t/: Studies at the phonemic and sub-phonemic level

Abstract This paper investigates the realization of word-final /t/ in conversational standard Dutch. First, based on a large number of word tokens (6747) annotated with broad phonetic transcription by an automatic transcription tool, we show that morphological properties of the words and their position in the utterances syntactic structure play a role for the presence versus absence of their final /t/. We also replicate earlier findings on the role of predictability (word frequency and bigram frequency with the following word) and provide a detailed analysis of the role of segmental context. Second, we analyze the detailed acoustic properties of word-final /t/ on the basis of a smaller number of tokens (486) which were annotated manually. Our data show that word and bigram frequency as well as segmental context also predict the presence of sub-phonemic properties. The investigations presented in this paper extend research on the realization of /t/ in spontaneous speech and have potential consequences for psycholinguistic models of speech production and perception as well as for automatic speech recognition systems.

Enhancing Speaker Discrimination at the Feature Level

This chapter describes a method for enhancing the differences between speaker classes at the feature level (feature enhancement) in an automatic speaker recognition system. The original Mel-frequency cepstral coefficient (MFCC) space is projected onto a new feature space by a neural network trained on a subset of speakers which is representative for the whole target population. The new feature space better discriminates between the target classes (speakers) than the original feature space. The chapter focuses on the method for selecting a representative subset of speakers, comparing several approaches to speaker selection. The effect of feature enhancement is tested both for clean and various noisy speech types to evaluate its applicability under practical conditions. It is shown that the proposed method leads to a substantial improvement in speaker recognition performance. The method can also be applied to other automatic speaker classification tasks.