Information theoretic syllable structure and its relation to the c-center effect

Established phonological theories postulate uniform syllable constituent structures. From a traditional hierarchical point of view, syllables are right branching implying a close connection between the nucleus and the coda. Articulatory Phonology in contrast suggests a stronger cohesion between onsets and nuclei than between nuclei and codas. This claim is empirically supported by the c-center effect which initially has been observed for onsets only. Nevertheless, recent studies revealed that this effect does not occur in all complex onsets and can also be observed in codas. To account for this structure non-uniformity, we propose an information theoretic approach to measure connection strengths between syllable constituents in terms of their pointwise mutual information. It turned out that the derived constituent structures correspond well to the empirical c-center findings on American English and German data. The results are discussed from a Usage-based Phonology perspective considering c-centers to be a frequency effect

Data-driven Extraction of Intonation Contour Classes

In this paper we introduce the first steps towards a new datadriven method for extraction of intonation events that does not require any prerequisite prosodic labelling. Provided with data segmented on the syllable constituent level it derives local and global contour classes by stylisation and subsequent clustering of the stylisation parameter vectors. Local contour classes correspond to pitch movements connected to one or several syllables and determine the local f0 shape. Global classes are connected to intonation phrases and determine the f0 register. Local classes initially are derived for syllabic segments, which are then concatenated incrementally by means of statistical language modelling of co-occurrence patterns. Due to its generality the method is in principal language independent and potentially capable to deal also with other aspects of prosody than intonation. 1

Improving Data Driven Part-of-Speech Tagging by Morphologic Knowledge Induction

We present a Markov part-of-speech tagger for which the P (w|t) emission probabilities of word w given tag t are replaced by a linear interpolation of tag emission probabilities given a list of representations of w. As word representations, string su#xes of w are cut o# at the local maxima of the Normalized Backward Successor Variety. This procedure allows for the derivation of linguistically meaningful string suffixes that may relate to certain POS labels. Since no linguistic knowledge is needed, the procedure is language independent. Basic Markov model part-of-speech taggers are significantly outperformed by our model

Automatisation of intonation modelling and its linguistic anchoring

This paper presents a fully machine-driven approach for intonation description and its linguistic interpretation. For this purpose,a new intonation model for bottom-up F0 contour analysis and synthesis is introduced, the CoPaSul model which is designed in the tradition of parametric, contour-based, and superpositional approaches. Intonation is represented by a superposition of global and local contour classes that are derived from F0 parameterisation. These classes were linguistically anchored with respect to information status by aligning them with a text which had been coarsely analysed for this purpose by means of NLP techniques. To test the adequacy of this data-driven interpretation a perception experiment was carried out, which confirmed 80% of the findings

Some matrix nearness problems suggested by Tikhonov regularization

The numerical solution of linear discrete ill-posed problems typically requires regularization, i.e., replacement of the available ill-conditioned problem by a nearby better conditioned one. The most popular regularization methods for problems of small to moderate size are Tikhonov regularization and truncated singular value decomposition (TSVD). By considering matrix nearness problems related to Tikhonov regularization, several novel regularization methods are derived. These methods share properties with both Tikhonov regularization and TSVD, and can give approximate solutions of higher quality than either one of these methods

A priori bounds and global bifurcation results for frequency combs modeled by the Lugiato-Lefever equation

In nonlinear optics $2\pi$-periodic solutions $a\in C^2([0,2\pi];\mathbb{C})$ of the stationary Lugiato-Lefever equation $-d a"= ({\rm i} -\zeta)a +|a|^2a-{\rm i} f$ serve as a model for frequency combs, which are optical signals consisting of a superposition of modes with equally spaced frequencies. We prove that nontrivial frequency combs can only be observed for special ranges of values of the forcing and detuning parameters $f$ and $\zeta$, as it has been previously documented in experiments and numerical simulations. E.g., if the detuning parameter $\zeta$ is too large then nontrivial frequency combs do not exist, cf. Theorem 2. Additionally, we show that for large ranges of parameter values nontrivial frequency combs may be found on continua which bifurcate from curves of trivial frequency combs. Our results rely on the proof of a priori bounds for the stationary Lugiato-Lefever equation as well as a detailed rigorous bifurcation analysis based on the bifurcation theorems of Crandall-Rabinowitz and Rabinowitz. We use the software packages AUTO and MATLAB to illustrate our results by numerical computations of bifurcation diagrams and of selected solutions