Publications

Abstract

Information in speech does not unfold discretely over time; perceptual cues are gradient and overlapped. However, this varies greatly across segments and environments: listeners cannot identify the affricate in /ptS/ until the frication, but information about the vowel in /li/ begins early. Unlike most prior studies, which have concentrated on subsets of language sounds, this study tests perception of every English segment in every phonetic environment, sampling perceptual identification at six points in time (13,470 stimuli/listener; 20 listeners). Results show that information about consonants after another segment is most localized for affricates (almost entirely in the release), and most gradual for voiced stops. In comparison to stressed vowels, unstressed vowels have less information spreading to
neighboring segments and are less well identified. Indeed, many vowels,
especially lax ones, are poorly identified even by the end of the following segment. This may partly reflect listeners’ familiarity with English vowels’ dialectal variability. Diphthongs and diphthongal tense vowels show the most sudden improvement in identification, similar to affricates among the consonants, suggesting that information about segments defined by acoustic change is highly localized. This large dataset provides insights into speech perception and data for probabilistic modeling of spoken word recognition.

Abstract

Recent studies using phoneme detection tasks have shown that spoken-language processing is neither facilitated nor interfered with by optional assimilation, but is inhibited by violation of obligatory assimilation. Interpretation of these results depends on an assessment of their generality, specifically, whether they also obtain when listeners are processing nonnative language. Two separate experiments are presented in which native listeners of German and native listeners of Dutch had to detect a target fricative in legal monosyllabic Dutch nonwords. All of the nonwords were correct realisations in standard Dutch. For German listeners, however, half of the nonwords contained phoneme strings which violate the German fricative assimilation rule. Whereas the Dutch listeners showed no significant effects, German listeners detected the target fricative faster when the German fricative assimilation was violated than when no violation occurred. The results might suggest that violation of assimilation rules does not have to make processing more difficult per se.

Abstract

In order to decode the message of a speaker, listeners have to recognize individual words in the speaker's utterance.

Abstract

Many people speak a second language next to their mother tongue. How do they learn this language and how does the brain process it compared to the native language? A second language can be learned without explicit instruction. Our brains automatically pick up grammatical structures, such as word order, when these structures are repeated frequently during learning. The learning takes place within hours or days and the same brain areas, such as frontal and temporal brain regions, that process our native language are very quickly activated. When people master a second language very well, even the same neuronal populations in these language brain areas are involved. This is especially the case when the grammatical structures are similar. In conclusion, it appears that a second language builds on the existing cognitive and neural mechanisms of the native language as much as possible.

Abstract

In the CLARIN infrastructure various national projects have started initiatives to allow users of the infrastructure to create chains or workflows of web services. The Component Metadata (CMD) core model for web services described in this paper tries to align the metadata descriptions of these various initiatives. This should allow chaining/workflow engines to find matching and invoke services. The paper describes the landscape of web services architectures and the state of the national initiatives. Based on this a CMD core model for CLARIN is proposed, which, within some limits, can be adapted to the specific needs of an initiative by the standard facilities of CMD. The paper closes with the current state and usage of the model and a look into the future.

Abstract

ISO Technical Committee 37, Terminology and other language and content resources, established an ISO 12620:2009 based Data Category Registry (DCR), called ISOcat (see http://www.isocat.org), to foster semantic interoperability of linguistic resources. However, this goal can only be met if the data categories are reused by a wide variety of linguistic resource types. A resource indicates its usage of data categories by linking to them. The small DC Reference XML vocabulary is used to embed links to data categories in XML documents. The link is established by an URI, which servers as the Persistent IDentifier (PID) of a data category. This paper discusses the efforts to mimic the same approach for RDF-based resources. It also introduces the RDF quad store based Relation Registry RELcat, which enables ontological relationships between data categories not supported by ISOcat and thus adds an extra level of linguistic knowledge.

Abstract

The ISOcat Data Category Registry contains basically a flat and easily extensible list of data category specifications. To foster reuse and standardization only very shallow relationships among data categories are stored in the registry. However, to assist crosswalks, possibly based on personal views, between various (application) domains and to overcome possible proliferation of data categories more types of ontological relationships need to be specified. RELcat is a first prototype of a Relation Registry, which allows storing arbitrary relationships. These relationships can reflect the personal view of one linguist or a larger community. The basis of the registry is a relation type taxonomy that can easily be extended. This allows on one hand to load existing sets of relations specified in, for example, an OWL (2) ontology or SKOS taxonomy. And on the other hand allows algorithms that query the registry to traverse the stored semantic network to remain ignorant of the original source vocabulary. This paper describes first experiences with RELcat and explains some initial design decisions.

Abstract

Automatic Language Identification of written texts is a well-established area of research in Computational Linguistics. State-of-the-art algorithms often rely on n-gram character models to identify the correct language of texts, with good results seen for European languages. In this paper we propose the use of a character n-gram model and a word n-gram language model for the automatic classification of two written varieties of Portuguese: European and Brazilian. Results reached 0.998 for accuracy using character 4-grams.

Abstract

Arbil is an application designed to create and manage metadata for research data and to arrange this data into a structure appropriate for archiving. The metadata is displayed in tables, which allows an overview of the metadata and the ability to populate and update many metadata sections in bulk. Both IMDI and Clarin metadata formats are supported and Arbil has been designed as a local application so that it can also be used offline, for instance in remote field sites. The metadata can be entered in any order or at any stage that the user is able; once the metadata and its data are ready for archiving and an Internet connection is available it can be exported from Arbil and in the case of IMDI it can then be transferred to the main archive via LAMUS (archive management and upload system).

Abstract

Introduction Speech research saw a dramatic change in paradigm in the 90-ies. While earlier the discussion was dominated by a phoneticians’ approach who knew about phenomena in the speech signal, the situation completely changed after stochastic machinery such as Hidden Markov Models [1] and Artificial Neural Networks [2] had been introduced. Speech processing was now dominated by a purely mathematic approach that basically ignored all existing knowledge about the speech production process and the perception mechanisms. The key was now to construct a large enough training set that would allow identifying the many free parameters of such stochastic engines. In case that the training set is representative and the annotations of the training sets are widely ‘correct’ we could assume to get a satisfyingly functioning recognizer. While the success of knowledge-based systems such as Hearsay II [3] was limited, the statistically based approach led to great improvements in recognition rates and to industrial applications.

Abstract

5.1 Einführung in den Forschungsbereich Die Psycholinguistik ist der Bereich der Linguistik, der sich mit dem Zusammenhang zwischen menschlicher Sprache und dem Denken und anderen mentalen Prozessen beschäftigt, d.h. sie stellt sich einer Reihe von essentiellen Fragen wie etwa (1) Wie schafft es unser Gehirn, im Wesentlichen akustische und visuelle kommunikative Informationen zu verstehen und in mentale Repräsentationen umzusetzen? (2) Wie kann unser Gehirn einen komplexen Sachverhalt, den wir anderen übermitteln wollen, in eine von anderen verarbeitbare Sequenz von verbalen und nonverbalen Aktionen umsetzen? (3) Wie gelingt es uns, in den verschiedenen Phasen des Lebens Sprachen zu erlernen? (4) Sind die kognitiven Prozesse der Sprachverarbeitung universell, obwohl die Sprachsysteme derart unterschiedlich sind, dass sich in den Strukturen kaum Universalien finden lassen?

Abstract

According to a widely-held view among various scholars, olfaction is inferior to other human senses. It is also believed by many that languages do not have words for describing smells. Data collected among the Maniq, a small population of nomadic foragers in southern Thailand, challenge the above claims and point to a great linguistic and cultural elaboration of odor. This article presents evidence of the importance of olfaction in indigenous rituals and beliefs, as well as in the lexicon. The results demonstrate the richness and complexity of the domain of smell in Maniq society and thereby challenge the universal paucity of olfactory terms and insignificance of olfaction for humans.

Abstract

In recent decades, neuroimaging studies on the neural infrastructure of language are usually (or mostly) conducted with certain on-line language processing tasks. These functional neuroimaging studies helped to localize the language areas in the brain and to investigate the brain activity during explicit language processing. However, little is known about what is going on with the language areas when the brain is ‘at rest’, i.e., when there is no explicit language processing running. Taking advantage of the fcMRI and DTI techniques, this thesis is able to investigate the language function ‘off-line’ at the neuronal network level and the connectivity among language areas in the brain. Based on patient studies, the traditional, classical model on the perisylvian language network specifies a “Broca’ area – Arcuate Fasciculus – Werinicke’s area” loop (Ojemann 1991). With the help of modern neuroimaging techniques, researchers have been able to track language pathways that involve more brain structures than are in the classical model, and relate them to certain language functions. In such a background, a large part of this thesis made a contribution to the study of the topology of the language networks. It revealed that the language networks form a topographical functional connectivity pattern in the left hemisphere for the right-handers. This thesis also revealed the importance of structural hubs, such as Broca’s and Wernicke’s areas, which have more connectivity to other brain areas and play a central role in the language networks. Furthermore, this thesis revealed both functionally and structurally lateralized language networks in the brain. The consistency between what is found in this thesis and what has been known from previous functional studies seems to suggest, that the human brain is optimized and ‘ready’ for the language function even when there is currently no explicit language-processing running.

Abstract

Automatic Language Identification of written texts is a well-established area of research in Computational Linguistics. State-of-the-art algorithms often rely on n-gram character models to identify the correct language of texts, with good results seen for European languages. In this paper we propose the use of a character n-gram model and a word n-gram language model for the automatic classification of two written varieties of Portuguese: European and Brazilian. Results reached 0.998 for accuracy using character 4-grams.

Abstract

This study presents a new language identification model for pluricentric languages that uses n-gram language models at the character and word level. The model is evaluated in two steps. The first step consists of the identification of two varieties of Spanish (Argentina and Spain) and two varieties of French (Quebec and France) evaluated independently in binary classification schemes. The second step integrates these language models in a six-class classification with two Portuguese varieties.

Abstract

Classifiers (currently also called 'depicting handshapes'), are observed in almost all signed languages studied to date and form a well-researched topic in sign language linguistics. Yet, these elements are still subject to much debate with respect to a variety of matters. Several different categories of classifiers have been posited on the basis of their semantics and the linguistic context in which they occur. The function(s) of classifiers are not fully clear yet. Similarly, there are differing opinions regarding their structure and the structure of the signs in which they appear. Partly as a result of comparison to classifiers in spoken languages, the term 'classifier' itself is under debate. In contrast to these disagreements, most studies on the acquisition of classifier constructions seem to consent that these are difficult to master for Deaf children. This article presents and discusses all these issues from the viewpoint that classifiers are linguistic elements.