The Karlsruhe-Verbmobil speech recognition engine

Finke, Michael; Geutner, Petra; Hild, Hermann; Kemp, Thomas; Ries, Klaus; Westphal, Martin

doi:10.1109/icassp.1997.599552

Cited by 70 publications

(36 citation statements)

References 8 publications

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“…Our MSA speech recognition system is based on the Janus Recognition Toolkit JRTk [9] and the IBIS decoder [10]. Before decoding the audio, an automatic segmentation step and a speaker clustering step is performed.…”

Section: System Descriptionmentioning

confidence: 99%

Automatic acquisition of grammatical types for nouns

Bel

Espeja

Marimon

2007

Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics

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The work 1 we present here is concerned with the acquisition of deep grammatical information for nouns in Spanish. The aim is to build a learner that can handle noise, but, more interestingly, that is able to overcome the problem of sparse data, especially important in the case of nouns. We have based our work on two main points. Firstly, we have used distributional evidences as features. Secondly, we made the learner deal with all occurrences of a word as a single complex unit. The obtained results show that grammatical features of nouns is a level of generalization that can be successfully approached with a Decision Tree learner.

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Section: System Descriptionmentioning

confidence: 99%

Automatic acquisition of grammatical types for nouns

Bel

Espeja

Marimon

2007

Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics

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“…The language model was trained using approximately 4M words. ASR decoding was performed using the Ibis decoder [7], which was developed as part of our Janus Recognition Toolkit (JRTk) [8].…”

Section: A System Architecturementioning

confidence: 99%

Pronunciation modeling for dialectal arabic speech recognition

Al-Haj

Hsiao

Lane

et al. 2009

2009 IEEE Workshop on Automatic Speech Recognition &Amp; Understanding

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Abstract-Short vowels in Arabic are normally omitted in written text which leads to ambiguity in the pronunciation. This is even more pronounced for dialectal Arabic where a single word can be pronounced quite differently based on the speaker's nationality, level of education, social class and religion. In this paper we focus on pronunciation modeling for Iraqi-Arabic speech. We introduce multiple pronunciations into the Iraqi speech recognition lexicon, and compare the performance, when weights computed via forced alignment are assigned to the different pronunciations of a word. Incorporating multiple pronunciations improved recognition accuracy compared to a single pronunciation baseline and introducing pronunciation weights further improved performance. Using these techniques an absolute reduction in word-error-rate of 2.4% was obtained compared to the baseline system.

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“…The two recognizers used in our system are CMU's Janus JRTk recognizer [1], [2], and M Modal's recognizer [3]. On a desktop or laptop, both recognizers may either run interchangeably or combined.…”

Section: ) Automatic Speech Recognitionmentioning

confidence: 99%

Flexible Speech Translation Systems

Schultz

Black

Vogel

et al. 2006

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Speech translation research has made significant progress over the years with many high-visibility efforts showing that translation of spontaneously spoken speech from and to diverse languages is possible and applicable in a variety of domains. As language and domains continue to expand, practical concerns such as portability and reconfigurability of speech come into play: system maintenance becomes a key issue and data is never sufficient to cover the changing domains over varying languages. In this paper, we discuss strategies to overcome the limits of today's speech translation systems. In the first part, we describe our layered system architecture that allows for easy component integration, resource sharing across components, comparison of alternative approaches, and the migration toward hybrid desktop/PDA or stand-alone PDA systems. In the second part, we show how flexibility and reconfigurability is implemented by more radically relying on learning approaches and use our English-Thai two-way speech translation system as a concrete example.

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The Karlsruhe-Verbmobil speech recognition engine

Cited by 70 publications

References 8 publications

Automatic acquisition of grammatical types for nouns

Automatic acquisition of grammatical types for nouns

Pronunciation modeling for dialectal arabic speech recognition

Flexible Speech Translation Systems

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