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Simultaneous translation of lectures and speeches

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Machine Translation

Abstract

With increasing globalization, communication across language and cultural boundaries is becoming an essential requirement of doing business, delivering education, and providing public services. Due to the considerable cost of human translation services, only a small fraction of text documents and an even smaller percentage of spoken encounters, such as international meetings and conferences, are translated, with most resorting to the use of a common language (e.g. English) or not taking place at all. Technology may provide a potentially revolutionary way out if real-time, domain-independent, simultaneous speech translation can be realized. In this paper, we present a simultaneous speech translation system based on statistical recognition and translation technology. We discuss the technology, various system improvements and propose mechanisms for user-friendly delivery of the result. Over extensive component and end-to-end system evaluations and comparisons with human translation performance, we conclude that machines can already deliver comprehensible simultaneous translation output. Moreover, while machine performance is affected by recognition errors (and thus can be improved), human performance is limited by the cognitive challenge of performing the task in real time.

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Abbreviations

AMI:

Meeting transcription data from Augmented Multi-party Interaction (see Table 3)

ASR:

Automatic speech recognition

BN:

Data from broadcast news corpora (see Table 3)

CHIL:

Computers in the human interaction loop

cMLLR:

Constrained maximum likelihood linear regression

DG:

Directorate general

EC:

European Commission

EM:

Expectation maximization

EPPS:

European Parliament plenary sessions

GALE:

Global Autonomous Language Exploitation

GWRD:

Data from Gigaword corpus (see Table 3)

HNSRD:

Data from UK Parliament debates (see Table 3)

ICSI:

(Data recorded at) International Computer Science Institute

JRTk:

Janus Recognition Toolkit

MLLR:

Maximum likelihood linear regression

MT:

Machine translation

MTG:

Meeting transcription data (see Table 3)

NIST:

National Institute of Standards and Technology—MT evaluation measure (Doddington 2002) Data recorded at NIST

OOV:

Out of vocabulary

PROC:

Data from conference proceedings (see Table 3)

RTF:

Real-time factor

RT-06S:

NIST 2006 Rich Transcription evaluation

RWTH:

Rheinisch-Westfälische Technische Hochschule (Aachen)

SMNR:

Lectures and seminars from the CHIL project

SMT:

Statistical machine translation

SRI:

Stanford Research Institute

SST:

Speech-to-speech translation

STR-DUST:

Speech TRanslation: Domain-Unlimited, Spontaneous and Trainable

SWB:

Data from switchboard transcriptions (see Table 3)

TC-STAR:

Technologies and Corpora for Speech-to-Speech-Translation

TED:

Translanguage English database

TH:

Technische Hochschule

TTS:

Text to speech

UKA-. . .:

See Table 3

UW-M:

See Table 3

VAD:

Voice activity detection

VTLN:

Vocal tract length normalization

WER:

Word error rate

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Authors and Affiliations

  1. International Center for Advanced Communication Technologies (InterACT), Fakultät für Informatik, Universität Karlsruhe (TH), Am Fasanengarten 5, 76131, Karlsruhe, Germany

    Christian Fügen, Alex Waibel & Muntsin Kolss

  2. International Center for Advanced Communication Technologies (InterACT), School of Computer Science, Carnegie Mellon University, 407 S. Craig Street, Pittsburgh, PA, 15213, USA

    Alex Waibel

  3. Mobile Technologies LLC, 619 Windsor Avenue, Pittsburgh, PA, 15221-4337, USA

    Christian Fügen & Alex Waibel

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  1. Christian Fügen
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Correspondence to Alex Waibel.

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Fügen, C., Waibel, A. & Kolss, M. Simultaneous translation of lectures and speeches. Machine Translation 21, 209–252 (2007). https://doi.org/10.1007/s10590-008-9047-0

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