Integrating Prosodics into a Language Model for Spoken Language Understanding of Thai

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Universal and language-specific processing : the case of prosody

A key question in the science of language is how speech processing can be influenced by both language-universal and language-specific mechanisms (Cutler, Klein, & Levinson, 2005). My graduate research aimed to address this question by adopting a crosslanguage approach to compare languages with different phonological systems. Of all components of linguistic structure, prosody is often considered to be one of the most language-specific dimensions of speech. This can have significant implications for our understanding of language use, because much of speech processing is specifically tailored to the structure and requirements of the native language. However, it is still unclear whether prosody may also play a universal role across languages, and very little comparative attempts have been made to explore this possibility. In this thesis, I examined both the production and perception of prosodic cues to prominence and phrasing in native speakers of English and Mandarin Chinese. In focus production, our research revealed that English and Mandarin speakers were alike in how they used prosody to encode prominence, but there were also systematic language-specific differences in the exact degree to which they enhanced the different prosodic cues (Chapter 2). This, however, was not the case in focus perception, where English and Mandarin listeners were alike in the degree to which they used prosody to predict upcoming prominence, even though the precise cues in the preceding prosody could differ (Chapter 3). Further experiments examining prosodic focus prediction in the speech of different talkers have demonstrated functional cue equivalence in prosodic focus detection (Chapter 4). Likewise, our experiments have also revealed both crosslanguage similarities and differences in the production and perception of juncture cues (Chapter 5). Overall, prosodic processing is the result of a complex but subtle interplay of universal and language-specific structure

Statistical morphological disambiguation with application to disambiguation of pronunciations in Turkish /

The statistical morphological disambiguation of agglutinative languages suffers from data sparseness. In this study, we introduce the notion of distinguishing tag sets (DTS) to overcome the problem. The morphological analyses of words are modeled with DTS and the root major part-of-speech tags. The disambiguator based on the introduced representations performs the statistical morphological disambiguation of Turkish with a recall of as high as 95.69 percent. In text-to-speech systems and in developing transcriptions for acoustic speech data, the problem occurs in disambiguating the pronunciation of a token in context, so that the correct pronunciation can be produced or the transcription uses the correct set of phonemes. We apply the morphological disambiguator to this problem of pronunciation disambiguation and achieve 99.54 percent recall with 97.95 percent precision. Most text-to-speech systems perform phrase level accentuation based on content word/function word distinction. This approach seems easy and adequate for some right headed languages such as English but is not suitable for languages such as Turkish. We then use a a heuristic approach to mark up the phrase boundaries based on dependency parsing on a basis of phrase level accentuation for Turkish TTS synthesizers

Stress and rhythm in modern Greek

SIGLEAvailable from British Library Document Supply Centre- DSC:D35991/81 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin

Semantic Analysis of the Thai Language

Computing and Information Science

PowerAqua: Open Question Answering on the Semantic Web

With the rapid growth of semantic information in the Web, the processes of searching and querying these very large amounts of heterogeneous content have become increasingly challenging. This research tackles the problem of supporting users in querying and exploring information across multiple and heterogeneous Semantic Web (SW) sources. A review of literature on ontology-based Question Answering reveals the limitations of existing technology. Our approach is based on providing a natural language Question Answering interface for the SW, PowerAqua. The realization of PowerAqua represents a considerable advance with respect to other systems, which restrict their scope to an ontology-specific or homogeneous fraction of the publicly available SW content. To our knowledge, PowerAqua is the only system that is able to take advantage of the semantic data available on the Web to interpret and answer user queries posed in natural language. In particular, PowerAqua is uniquely able to answer queries by combining and aggregating information, which can be distributed across heterogeneous semantic resources. Here, we provide a complete overview of our work on PowerAqua, including: the research challenges it addresses; its architecture; the techniques we have realised to map queries to semantic data, to integrate partial answers drawn from different semantic resources and to rank alternative answers; and the evaluation studies we have performed, to assess the performance of PowerAqua. We believe our experiences can be extrapolated to a variety of end-user applications that wish to open up to large scale and heterogeneous structured datasets, to be able to exploit effectively what possibly is the greatest wealth of data in the history of Artificial Intelligence

THE USE OF SEGMENTATION CUES IN SECOND LANGUAGE LEARNERS OF ENGLISH

This dissertation project examined the influence of language typology on the use of segmentation cues by second language (L2) learners of English. Previous research has shown that native English speakers rely more on sentence context and lexical knowledge than segmental (i.e. phonotactics or acoustic-phonetics) or prosodic cues (e.g., word stresss) in native language (L1) segmentation. However, L2 learners may rely more on segmental and prosodic cues to identify word boundaries in L2 speech since it may require high lexical and syntactic proficiency in order to use lexical cues efficiently. The goal of this dissertation was to provide empirical evidence for the Revised Framework for L2 Segmentation (RFL2) which describes the relative importance of different levels of segmentation cues. Four experiments were carried out to test the hypotheses made by RFL2. Participants consisted of four language groups including native English speakers and L2 learners of English with Mandarin, Korean, or Spanish L1s. Experiment 1 compared the use of stress cues and lexical knowledge while Experiment 2 compared the use of phonotactic cues and lexical knowledge. Experiment 3 compared the use of phonotactic cues and semantic cues while Experiment 4 compared the use of stress cues and sentence context. Results showed that L2 learners rely more on segmental cues than lexical knowledge or semantic cues. L2 learners showed cue interaction in both lexical and sublexical levels whereas native speakers appeared to use the cues independently. In general, L2 learners appeared to have acquired sensitivity to the segmentation cues used in L2, although they still showed difficulty with specific aspects in each cue based on L1 characteristics. The results provided partial support for RFL2 in which L2 learners' use of sublexical cues was influenced by L1 typology. The current dissertation has important pedagogical implication as findings may help identify cues that can facilitate L2 speech segmentation and comprehension