Publications

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

A new psycholinguistically motivated and neural network based model of human word recognition is presented. In contrast to earlier models it uses real speech as input. At the word layer acoustical and temporal information is stored by sequences of connected sensory neurons which pass on sensor potentials to a word neuron. In experiments with a small lexicon which includes groups of very similar word forms, the model meets high standards with respect to word recognition and simulates a number of wellknown psycholinguistical effects.

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

Set is widely used as a kind of basic data structure. However, when it is used for large scale data set the cost of storage, search and transport is overhead. The bloom filter uses a fixed size bit string to represent elements in a static set, which can reduce storage space and search cost that is a fixed constant. The time-space efficiency is achieved at the cost of a small probability of false positive in membership query. However, for many applications the space savings and locating time constantly outweigh this drawback. Dynamic bloom filter (DBF) can support concisely representation and approximate membership queries of dynamic set instead of static set. It has been proved that DBF not only possess the advantage of standard bloom filter, but also has better features when dealing with dynamic set. This paper proposes a time-dependent multiple bit-strings bloom filter (TMBF) which roots in the DBF and targets on dynamic incremental set. TMBF uses multiple bit-strings in time order to present a dynamic increasing set and uses backward searching to test whether an element is in a set. Based on the system logs from a real P2P file sharing system, the evaluation shows a 20% reduction in searching cost compared to DBF.

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.