Interactive spoken-language processing in a hybrid connectionist system

Wermter, Stefan; Weber, Volker

doi:10.1109/2.511970

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…After preliminary successful case studies with transcripts we have developed the screen system for using knowledge generated from a speech recognizer. In previous work, we gave a brief summary of screen with a speci c focus on segmentation parsing and dialog act processing (Wermter & Weber, 1996a). In this paper, we focus on a detailed description of screen's architecture, the at syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the in uence of noisy or incomplete input.…”

Section: Flat Representations Learned In a Hybrid Connectionist Framementioning

confidence: 99%

“…This performance allows us the computation of a syntactic and semantic plausibility in syn-speech-error and sem-speech-error. Based on the combined acoustic, syntactic, and semantic knowledge, rst tests on the 184 turns show that the accuracy of the constructed sentence hypotheses of screen could be increased by about 30% using acoustic and syntactic plausibilities and by about 50% using acoustic, syntactic, and semantic plausibilities (Wermter & Weber, 1996a). 11.…”

Section: Improvement In the Hypothesis Spacementioning

confidence: 99%

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SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

Wermter

Weber

1997

jair

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Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken-language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component.In contrast, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a at analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a at connectionist analysis. This screening approach aims at supporting speech and language processing by using 1 data-driven learning and 2 robustness of connectionist networks. In order to test this approach, we h a ve developed the screen system which i s based on this new robust, learned and at analysis.In this paper, we focus on a detailed description of screen's architecture, the at syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the in uence of noisy or incomplete input. The main result of this paper is that at representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show h o w the fault-tolerance and learning capability of connectionist networks can support a at analysis for providing more robust spoken-language processing within an overall hybrid symbolic connectionist framework.

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Section: Flat Representations Learned In a Hybrid Connectionist Framementioning

confidence: 99%

Section: Improvement In the Hypothesis Spacementioning

confidence: 99%

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

Wermter

Weber

1997

jair

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“…The research presented here is embedded in a larger effort for examining hybrid eonnectionist learning capabilities for the analysis of spoken language at various acoustic, syntactic, semantic and pragmatic levels. To investigate hybrid connectionist architectures for speech/language analysis we devek)l)ed the SCREEN system (Symbolic Connectionist ll.obust Enterprise for Natural language) (Wermter and Weber, 1996). For the task of analyzing spontancous language we pursue a shallow screening analysis which uses prima,> ily flat representations (like category sequences) wherever possible.…”

Section: The Overall Approachmentioning

confidence: 99%

Learning dialog act processing

Wermter

Löchel

1996

Proceedings of the 16th Conference on Computational Linguistics -

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In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parse,: with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably with the statistical average plansibility method, produces a segmentation and dialog act assignment for all utteranccs in a robust manner, and redaces knowledge engineering since it can be bootstrapped from rather small corpora. Therefore, we consider this new approach as very promising for learning dialog act processing.

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The BABY Project

Rogers¹,

Lefley²

1999

Machine Conversations

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Interactive spoken-language processing in a hybrid connectionist system

Cited by 5 publications

References 8 publications

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

Learning dialog act processing

The BABY Project

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