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Notions such as “corpus-driven” versus “theory-driven” bring into focus the specific role of corpora in linguistic research. As for phonology with its intrinsic focus on abstract categorical representation, there is a question of how a strictly corpus-driven approach can yield insight into relevant structures. Here we argue for a more theory-driven approach to phonology based on the concept of a phonological grammar in terms of interacting constraints. Empirical validation of such grammars comes from the potential convergence of the evidence from various sources including typological data, neutralization patterns, and in particular patterns observed in the creative use of language such as acronym formation, loanword adaptation, poetry, and speech errors. Further empirical validation concerns specific predictions regarding phonetic differences among opposition members, paradigm uniformity effects, and phonetic implementation in given segmental and prosodic contexts. Corpora in the narrowest sense (i.e. “raw” data consisting of spontaneous speech produced in natural settings) are useful for testing these predictions, but even here, special purpose-built corpora are often necessary.
In diesem Beitrag werden drei quantitative Studien vorgestellt, mit deren Hilfe untersucht wird, ob neben dem robusten Längenunterschied auch Qualitätsunterschiede für die deutschen <a>-Laute vorhanden sind (z.B. <Saat> versus <satt>). Auf Basis von ausgewählten Korpora und instrumentalphonetischen Messungen kann dieser Zusammenhang bestätigt werden. Zudem zeigen sich signifikante Unterschiede in den dynamischen
Verläufen der beiden Vokale.
We present evidence for the analysis of the vowels in English <say> and <so> as biphonemic diphthongs /ɛi/ and /əu/, based on neutralization patterns, regular alternations, and foot structure. /ɛi/ and /əu/ are hence structurally on a par with the so called “true diphthongs” /ɑi/, /ɐu/, /ɔi/, but also share prosodic organization with the monophthongs /i/ and /u/. The phonological evidence is supported by dynamic measurements based on the American English TIMIT database.
Calculations of F2-slopes proved to be especially suited to distinguish the relevant groups in accordance with their phonologically motivated prosodic organizations.
This paper is concerned with a novel methodology for generating phonetic questions used in tree-based state tying for speech recognition. In order to implement a speech recognition system, language-dependent knowledge which goes beyond annotated material is usually required. The approach presented here generates phonetic questions for decision trees are based on a feature table that summarizes the articulatory characteristics of each sound. On the one hand, this method allows better language-specific triphone models to be defined given only a feature-table as linguistic input. On the other hand, the feature-table approach facilitates efficient definition of triphone models for other languages since again only a feature table for this language is required. The approach is exemplified with speech recognition systems for English and Thai.
The Partitur Format at BAS
(1997)
Most spoken language resources are produced and disseminated together with symbolic information relating to the speech signal. These are for instance orthographic transcript labeling and segmentation on the phonologic phoneti prosodic phrasal level. Most of the known formats for these symbolic data are defined in a ‘closed form’ that is not fexible enough to allow simple and platform independent processing and easy extensions.
At the Bavarian Archive for Speech Signals (BAS) a new format has been developed and used over the last few years that shows some significant advantages over other existing formats. This paper describes the basic principles behind this format discusses briefly the advantages and gives detailed definitions of the description levels used so far.
This paper outlines the generation process of a specifi computational linguistic representation termed the Multilingual Time Map, conceptually a multi-tape finit state transducer encoding linguistic data at different levels of granularity. The fi st component acquires phonological data from syllable labeled speech data, the second component define feature profiles the third component generates feature hierarchies and augments the acquired data with the define feature profiles and the fourth component displays the Multilingual Time Map as a graph.
HMMs are the dominating technique used in speech recognition today since they perform well in overall phone recognition. In this paper, we show the comparison of HMM methods and machine learning techniques, such as neural networks, decision trees and ensemble classifiers with boosting and bagging in the task of articulatory-acoustic feature classification. The experimental results show that HMM methods work well for the classification of such features as vocalic. However, decision tree and bagging outperform HMMs for the fricative classification task since the data skewness is much higher than for the feature vocalic classification task. This demonstrates that HMMs do not perform as well as decision trees and bagging in highly skewed data settings.