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

SKOPE: A connectionist/symbolic architecture of spoken Korean processing

Spoken language processing requires speech and natural language integration. Moreover, spoken Korean calls for unique processing methodology due to its linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic spoken Korean processing engine, which emphasizes that: 1) connectionist and symbolic techniques must be selectively applied according to their relative strength and weakness, and 2) the linguistic characteristics of Korean must be fully considered for phoneme recognition, speech and language integration, and morphological/syntactic processing. The design and implementation of SKOPE demonstrates how connectionist/symbolic hybrid architectures can be constructed for spoken agglutinative language processing. Also SKOPE presents many novel ideas for speech and language processing. The phoneme recognition, morphological analysis, and syntactic analysis experiments show that SKOPE is a viable approach for the spoken Korean processing.Comment: 8 pages, latex, use aaai.sty & aaai.bst, bibfile: nlpsp.bib, to be presented at IJCAI95 workshops on new approaches to learning for natural language processin

Reverse production effect: Children recognize novel words better when they are heard rather than produced

This is the peer reviewed version of the following article: Tania S. Zamuner, Stephanie Strahm, Elizabeth Morin-Lessard, and Michael P. A. Page, 'Reverse production effect: children recognize novel words better when they are heard rather than produced', Developmental Science, which has been published in final form at DOI 10.1111/desc.12636. Under embargo until 15 November 2018. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.This research investigates the effect of production on 4.5- to 6-year-old children’s recognition of newly learned words. In Experiment 1, children were taught four novel words in a produced or heard training condition during a brief training phase. In Experiment 2, children were taught eight novel words, and this time training condition was in a blocked design. Immediately after training, children were tested on their recognition of the trained novel words using a preferential looking paradigm. In both experiments, children recognized novel words that were produced and heard during training, but demonstrated better recognition for items that were heard. These findings are opposite to previous results reported in the literature with adults and children. Our results show that benefits of speech production for word learning are dependent on factors such as task complexity and the developmental stage of the learner.Peer reviewedFinal Accepted Versio

Modality-Independent Effects of Phonological Neighborhood Structure on Initial L2 Sign Language Learning

The goal of the present study was to characterize how neighborhood structure in sign language influences lexical sign acquisition in order to extend our understanding of how the lexicon influences lexical acquisition in both sign and spoken languages. A referent-matching lexical sign learning paradigm was administered to a group of 29 hearing sign language learners in order to create a sign lexicon. The lexicon was constructed based on exposures to signs that resided in either sparse or dense handshape and location neighborhoods. The results of the current study indicated that during the creation of the lexicon signs that resided in sparse neighborhoods were learned better than signs that resided in dense neighborhoods. This pattern of results is similar to what is seen in child first language acquisition of spoken language. Therefore, despite differences in child first language and adult second language acquisition, these results contribute to a growing body of literature that implicates the phonological features that structure of the lexicon is influential in initial stages of lexical acquisition for both spoken and sign languages. This is the first study that uses an innovated lexicon-construction methodology to explore interactions between phonology and the lexicon in L2 acquisition of sign language

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing

We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of effects previously observed in behavioural experiments. We develop two information-theoretical measures of the distribution of usages of a morpheme or word, and use them to predict responses in three visual lexical decision datasets investigating inflectional morphology and polysemy. Our model offers a neurophysiological basis for the effects of morpho-semantic neighbourhoods. These results demonstrate how distributed patterns of activation naturally result in the arisal of symbolic structures. We conclude by arguing that the modelling framework exemplified here, is a powerful tool for integrating behavioural and neurophysiological results