Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean

A new tightly coupled speech and natural language integration model is presented for a TDNN-based continuous possibly large vocabulary speech recognition system for Korean. Unlike popular n-best techniques developed for integrating mainly HMM-based speech recognition and natural language processing in a {\em word level}, which is obviously inadequate for morphologically complex agglutinative languages, our model constructs a spoken language system based on a {\em morpheme-level} speech and language integration. With this integration scheme, the spoken Korean processing engine (SKOPE) is designed and implemented using a TDNN-based diphone recognition module integrated with a Viterbi-based lexical decoding and symbolic phonological/morphological co-analysis. Our experiment results show that the speaker-dependent continuous {\em eojeol} (Korean word) recognition and integrated morphological analysis can be achieved with over 80.6% success rate directly from speech inputs for the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer processing of oriental language journa

Research in the Language, Information and Computation Laboratory of the University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLiFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. Naturally, this introduction cannot spell out all the connections between these abstracts; we invite you to explore them on your own. In fact, with this issue it’s easier than ever to do so: this document is accessible on the “information superhighway”. Just call up http://www.cis.upenn.edu/~cliff-group/94/cliffnotes.html In addition, you can find many of the papers referenced in the CLiFF Notes on the net. Most can be obtained by following links from the authors’ abstracts in the web version of this report. The abstracts describe the researchers’ many areas of investigation, explain their shared concerns, and present some interesting work in Cognitive Science. We hope its new online format makes the CLiFF Notes a more useful and interesting guide to Computational Linguistics activity at Penn

A Modern Analysis of Consonant Harmony in Theoretical Frameworks

Consonant Harmony (CH) is a process that commonly characterizes the phonology of a child\u27s first language acquisition. CH is the means by which non-adjacent consonants partly assimilate to one another in the primary place of articulation of features, e.g. the word dog is pronounced as gog. This paper provides a modern interpretation of the treatment of CH under successive phonological frameworks: early generative, autosegmental, connectionist, and Optimality Theory (OT). The review highlights the inter-child, intra-child, and cross-linguistic differences between child and adult CH in order to emphasize the contrasts appearing in child language. More recent accounts propose a comprehensive analysis of CH that takes into account inter-child, intra-child and cross-linguistic variation. Optimality Theory is shown to provide the most detailed analytical framework to account for CH. The study concludes by suggesting directions for further research

Is Hanja represented in the Korean mental lexicon?: Encoding cross-script semantic cohorts in the representation of Sino-Korean

Korean can be transcribed in two different scripts, one alphabetic (Hangul) and one logographic (Hanja). How does the mental lexicon represent the contributions of multiple scripts? Hangul’s highly transparent one-to-one relationship between spellings and sounds creates homophones in spoken Korean that are also homographs in Hangul, which can only be disambiguated through Hanja. We thus tested whether native speakers encoded the semantic contributions of the different Hanja characters sharing the same homographic form in Hangul in their mental representation of Sino-Korean. Is processing modulated by the number of available meanings, that is, the size of the semantic cohort? In two cross-modal lexical decision tasks with semantic priming,participants were presented with auditory primes that were either syllables (Experiment 1) or full Sino-Korean words (Experiment 2), followed by visual Sino-Korean full word targets. In Experiment 1, reaction times were not significantly modulated by the size of the semantic cohort. However, in Experiment 2, we observed significantly faster reaction times for targets preceded by primes with larger semantic cohorts. We discuss these findings in relation to the structure of the mental lexicon for bi-scriptal languages and the representation of semantic cohorts across different scripts.1. Introduction 2. Hanja and Hangul during processing 3. Experiment 1: Cross-modal fragment priming 3.1. Method 3.1.1. Participants 3.1.2. Materials and design 3.1.3. Procedure 3.2. Results 3.3. Discussion 4. Experiment 2: Cross-modal full word priming 4.1. Method 4.1.1. Participants 4.1.2. Materials and design 4.1.3. Procedure 4.2. Results 4.3. Discussion 5. General discussion 6. Conclusion

CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

The Computational Linguistics Feedback Forum (CLIFF) is a group of students and faculty who gather once a week to discuss the members\u27 current research. As the word feedback suggests, the group\u27s purpose is the sharing of ideas. The group also promotes interdisciplinary contacts between researchers who share an interest in Cognitive Science. There is no single theme describing the research in Natural Language Processing at Penn. There is work done in CCG, Tree adjoining grammars, intonation, statistical methods, plan inference, instruction understanding, incremental interpretation, language acquisition, syntactic parsing, causal reasoning, free word order languages, ... and many other areas. With this in mind, rather than trying to summarize the varied work currently underway here at Penn, we suggest reading the following abstracts to see how the students and faculty themselves describe their work. Their abstracts illustrate the diversity of interests among the researchers, explain the areas of common interest, and describe some very interesting work in Cognitive Science. This report is a collection of abstracts from both faculty and graduate students in Computer Science, Psychology and Linguistics. We pride ourselves on the close working relations between these groups, as we believe that the communication among the different departments and the ongoing inter-departmental research not only improves the quality of our work, but makes much of that work possible

CLiFF Notes: Research in the Language Information and Computation Laboratory of The University of Pennsylvania

This report takes its name from the Computational Linguistics Feedback Forum (CLIFF), an informal discussion group for students and faculty. However the scope of the research covered in this report is broader than the title might suggest; this is the yearly report of the LINC Lab, the Language, Information and Computation Laboratory of the University of Pennsylvania. It may at first be hard to see the threads that bind together the work presented here, work by faculty, graduate students and postdocs in the Computer Science, Psychology, and Linguistics Departments, and the Institute for Research in Cognitive Science. It includes prototypical Natural Language fields such as: Combinatorial Categorial Grammars, Tree Adjoining Grammars, syntactic parsing and the syntax-semantics interface; but it extends to statistical methods, plan inference, instruction understanding, intonation, causal reasoning, free word order languages, geometric reasoning, medical informatics, connectionism, and language acquisition. With 48 individual contributors and six projects represented, this is the largest LINC Lab collection to date, and the most diverse