Integrating different learning approaches into a multilingual spoken language translation system

Geutner, Petra; Suhm, Bernhard; Buo, F.D.; Kemp, Thomas; Mayfield, Laura; McNair, A.; Rogina, Ivica; Schultz, Tanja; Sloboda, T.; Ward, Wayne H.; Woszczyna, Monika; Waibel, Alex

doi:10.1007/3-540-60925-3_42

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…Table 1 shows a detailed description of all available data. All recognition results reported have been performed with the JANUS system [1].…”

Section: Experiments 31 Databasementioning

confidence: 99%

Introducing linguistic constraints into statistical language modeling

Geutner¹

1996

4th International Conference on Spoken Language Processing (ICSLP 1996)

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Building robust stochastic language models is a major issue in speech recognition systems. Conventional word-based n-gram models do not capture any linguistic constraints inherent in speech. In this paper the notion of function and content w ords (open/closed word classes) is used to provide linguistic knowledge that can be incorporated into language models. Function words are articles, prepositions, personal pronouns { content w ords are nouns, verbs, adjectives and adverbs. Based on this class denition resulting in function and content w ord markers, a new language model is dened. A combination of the word-based model with this new model will be introduced. The combined model shows modest improvements both in perplexity results and recognition performance.

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“…Table 1 shows a detailed description of all available data. All recognition results reported have been performed with the JANUS system [1].…”

Section: Experiments 31 Databasementioning

confidence: 99%

Introducing linguistic constraints into statistical language modeling

Geutner¹

1996

4th International Conference on Spoken Language Processing (ICSLP 1996)

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“…Recently the elds of speech processing as well as language processing have both seen e orts to examine the possibility of integrating speech and language processing (von Hahn & Pyka, 1992;Jurafsky et al, 1994b;Waibel et al, 1992;Ward, 1994;Menzel, 1994;Geutner et al, 1996;. While new and large speech and language corpora are being developed rapidly, new techniques have to be examined which particularly support properties of both speech and language processing.…”

Section: Introductionmentioning

confidence: 99%

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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Interactive spoken-language processing in a hybrid connectionist system

Wermter

Weber

1996

Computer

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Integrating different learning approaches into a multilingual spoken language translation system

Cited by 6 publications

References 12 publications

Introducing linguistic constraints into statistical language modeling

Introducing linguistic constraints into statistical language modeling

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

Interactive spoken-language processing in a hybrid connectionist system

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