JANUS 93: towards spontaneous speech translation

Woszczyna, Monika; Aoki-Waibel, N.; Buo, F.D.; Coccaro, Noah; Horiguchi, Keiko; Kemp, Thomas; Lavie, Alon; McNair, A.; Polzin, Thomas; Rogina, Ivica; Rosé, Carolyn P.; Schultz, Tanja; Suhm, Bernhard; Tomita, Masaru; Waibel, Alex

doi:10.1109/icassp.1994.389285

Cited by 36 publications

(14 citation statements)

References 9 publications

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“…It uses a full parser first, and then a partial parser when the full parser does not return a result. CMUs Janus parser (Woszczyna et al, 1994) uses a similar strategy.…”

Section: Multi-parser Qasmentioning

confidence: 99%

Versatile question answering systems: seeing in synthesis

Mittal

2011

IJIIDS

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Recent advances in massive information storage and growth of internet have led to increased necessity of tools such as search engines and QA systems to get meaningful answers from the vast amount of information. The complex questions appearing in real-life generally require multiple techniques or sub-division of question at various levels. Several practical systems have been developed to explore this. This paper reviews state-of-art QA systems which provide enhanced and versatile functionality at various levels in their architecture and then makes the following contributions:1 it provides useful overview of research trends and recent developments in the area of QA 2 the paper introduces and defines basic design parameters for any QA system 3 it resolves semantic heterogeneity by clarifying the meaning and context in which different terms are used by different researchers 4 it classifies the existing QA systems in broad groups, based on the point at which versatility (multiplicity) is introduced/handled in the system 5 by developing a unified view of existing frontiers of QA systems, it provides clear directions for future research and development in the area.

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“…It uses a full parser first, and then a partial parser when the full parser does not return a result. CMUs Janus parser (Woszczyna et al, 1994) uses a similar strategy.…”

Section: Multi-parser Qasmentioning

confidence: 99%

Versatile question answering systems: seeing in synthesis

Mittal

2011

IJIIDS

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“…We developed all workers in-house. The audio is being transcribed using the Janus Recognition Toolkit (JRTk) (Woszczyna et al, 1994), which features the IBIS single-pass decoder (Soltau et al, 2001). The acoustic model was trained using several hundred hours of recordings from lectures and talks.…”

Section: System Descriptionmentioning

confidence: 99%

Lecture Translator - Speech translation framework for simultaneous lecture translation

Müller

Nguyen

Niehues

et al. 2016

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstratio

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Foreign students at German universities often have difficulties following lectures as they are often held in German. Since human interpreters are too expensive for universities we are addressing this problem via speech translation technology deployed in KIT's lecture halls. Our simultaneous lecture translation system automatically translates lectures from German to English in real-time. Other supported language directions are English to Spanish, English to French, English to German and German to French. Automatic simultaneous translation is more than just the concatenation of automatic speech recognition and machine translation technology, as the input is an unsegmented, practically infinite stream of spontaneous speech. The lack of segmentation and the spontaneous nature of the speech makes it especially difficult to recognize and translate it with sufficient quality. In addition to quality, speed and latency are of the utmost importance in order for the system to enable students to follow lectures. In this paper we present our system that performs the task of simultaneous speech translation of university lectures by performing speech translation on a stream of audio in real-time and with low latency. The system features several techniques beyond the basic speech translation task, that make it fit for real-world use. Examples of these features are a continuous stream speech recognition without any prior segmentation of the input audio, punctuation prediction, run-on decoding and run-on translation with continuously updating displays in order to keep the latency as low as possible.

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“…The speech-to-speech translation system JANUS-3 [7] is a joint eort of the Interactive Systems Labs at Carnegie Mellon University, Pittsburgh, and at the University of Karlsruhe, Germany. The baseline speech recognition component of JANUS-3 uses mixture-gaussian densities with a scalable amount of parameter tying.…”

Section: Neural Net Classiermentioning

confidence: 99%

Confidence measures for spontaneous speech recognition

Schaaf¹,

Kemp²

1997 IEEE International Conference on Acoustics, Speech, and Signal Processing

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For many practical applications of speech recognition systems, it is desirable to have an estimate of condence for each h ypothesized word, i.e. to have an estimate of which words of the output of the speech recognizer are likely to be correct and which are not reliable. We describe the development of the measure of condence tagger JANKA, which i s able to provide condence information for the words in the output of the speech recognizer JANUS-3-SR. On a spontaneous german human-to-human database, JANKA achieves a tagging accuracy of 90% at a baseline word accuracy of 82%.

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JANUS 93: towards spontaneous speech translation

Cited by 36 publications

References 9 publications

Versatile question answering systems: seeing in synthesis

Versatile question answering systems: seeing in synthesis

Lecture Translator - Speech translation framework for simultaneous lecture translation

Confidence measures for spontaneous speech recognition

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