Publications (10)4.63 Total impact
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Conference Proceeding: Recent improvements in BBN's English/Iraqi speech-to-speech translation system
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ABSTRACT: We report on recent improvements in our English/Iraqi Arabic speech-to-speech translation system. User interface improvements include a novel parallel approach to user confirmation which makes confirmation cost-free in terms of dialog duration. Automatic speech recognition improvements include the incorporation of state-of-the-art techniques in feature transformation and discriminative training. Machine translation improvements include a novel combination of multiple alignments derived from various pre-processing techniques, such as Arabic segmentation and English word compounding, higher order N-grams for target language model, and use of context in form of semantic classes and part-of-speech tags.Spoken Language Technology Workshop, 2008. SLT 2008. IEEE; 01/2009 -
Conference Proceeding: Name aware speech-to-speech translation for English/Iraqi
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ABSTRACT: In this paper, we describe a novel approach that exploits intra-sentence and dialog-level context for improving translation performance on spoken Iraqi utterances that contain named entities (NEs). Dialog-level context is used to predict whether the Iraqi response is likely to contain names and the intra-sentence context is used to determine words that are named entities. While we do not address the problem of translating out-of-vocabulary (OOV) NEs in spoken utterances, we show that our approach is capable of translating OOV names in text input. To demonstrate efficacy of our approach, we present results on internal test set as well as the 2008 June DARPA TRANSTAC name evaluation set.Spoken Language Technology Workshop, 2008. SLT 2008. IEEE; 01/2009 -
Conference Proceeding: Semantic translation error rate for evaluating translation systems
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ABSTRACT: In this paper, we introduce a new metric which we call the semantic translation error rate, or STER, for evaluating the performance of machine translation systems. STER is based on the previously published translation error rate (TER) (Snover et al., 2006) and METEOR (Banerjee and Lavie, 2005) metrics. Specifically, STER extends TER in two ways: first, by incorporating word equivalence measures (WordNet and Porter stemming) standardly used by METEOR, and second, by disallowing alignments of concept words to non-concept words (aka stop words). We show how these features make STER alignments better suited for human-driven analysis than standard TER. We also present experimental results that show that STER is better correlated to human judgments than TER. Finally, we compare STER to METEOR, and illustrate that METEOR scores computed using the STER alignments have similar statistical properties to METEOR scores computed using METEOR alignments.Automatic Speech Recognition & Understanding, 2007. ASRU. IEEE Workshop on; 01/2008 -
Conference Proceeding: Real-Time Speech-to-Speech Translation for PDAs
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ABSTRACT: In this paper we present a speech-to-speech translation system configured for translingual communication in English and colloquial Iraqi on a mobile, handheld device. The end-to-end system employs a medium/large vocabulary n-gram speech recognition engine for recognizing English and colloquial Iraqi, a question canonicalizer for mapping a recognized English question or command to one of the questions supported in the system, a concept translation engine for translating recognized Iraqi text, and a text-to-speech synthesis engine for playing back the English translation for the Iraqi to the English speaker. In addition to describing the system architecture and the functionality of the components, we present optimization techniques that enable low-latency, real-time speech recognition on low-power hardware platforms.Portable Information Devices, 2007. PORTABLE07. IEEE International Conference on; 06/2007 -
Conference Proceeding: DESIGN AND EVALUATION OF THE 2006 BBN ENGLISH/IRAQI TWO-WAY SPEECH TRANSLATION SYSTEM
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ABSTRACT: In this paper, we present a 2-way speech-to-speech translation system for English and Iraqi colloquial Arabic, the dialect of Arabic spoken by ordinary people in Iraq. The application domain of the system is military force protection, including municipal services surveys, detainee screening, and descriptions of people, houses, vehicles, etc. The system uses statistical speech recognition, and a combination of prerecorded questions and statistical machine translation with speech synthesis to translate the speech recognition output. We present evaluation results, along with an analysis of the gap between Iraqi-to-English and English-to-Iraqi translation performance.Spoken Language Technology Workshop, 2006. IEEE; 01/2007 -
Conference Proceeding: Hidden understanding models for statistical sentence understanding
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ABSTRACT: We describe the first sentence understanding system that is completely based on learned methods both for understanding individual sentences, and determining their meaning in the context of preceding sentences. We divide the problem into three stages: semantic parsing, semantic classification, and discourse modeling. Each of these stages requires a different model. When we ran this system on the last test (December, 1994) of the ARPA Air Travel Information System (ATIS) task, we achieved a 13.7% error rate. The error rate for those sentences that are context-independent (class A) was 9.7%Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on; 05/1997 · 4.63 Impact Factor -
Conference Proceeding: Language understanding using hidden understanding models
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ABSTRACT: Describes a sentence understanding system that is completely based on learned methods both for understanding individual sentences and for determining their meaning in the context of the preceding sentences. We describe the models used for each of three stages in the understanding: semantic parsing, semantic classification and discourse modeling. When we ran this system on the December 1994 test of the ARPA Air Travel Information System (ATIS) task, we achieved a 14.5% error rate. The error rate for those sentences that are context-independent (class A) was 9.5%Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on; 11/1996 -
Conference Proceeding: The BBN/HARC spoken language understanding system
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ABSTRACT: The design and performance of a complete spoken language understanding system under development at BBN are described. The system, dubbed HARC (Hear And Respond to Continuous speech), successfully integrates state-of-the-art speech recognition and natural language understanding subsystems. The system has been tested extensively on a restricted airline travel information (ATIS) domain with a vocabulary of about 2000 words. HARC is implemented in portable, high-level software that runs in real time on today's workstations to support interactive online human-machine dialogs. No special-purpose hardware is required other than an A/D (analog-to-digital) converter to digitize the speech. The system works well for any native speaker of American English and does not require any enrollment data from the users. Results of formal DARPA tests in Feb. and Nov. 1992 are presentedAcoustics, Speech, and Signal Processing, 1993. ICASSP-93., 1993 IEEE International Conference on; 05/1993 -
Article: The IRUS transportable natural language database interface
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ABSTRACT: An abstract is not available. -
Article: Rapid Development of an English/Farsi Speech-to-Speech Translation System
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ABSTRACT: Significant advances have been achieved in Speech-to-Speech (S2S) translation systems in recent years. However, rapid configuration of S2S systems for low-resource language pairs and domains remains a challenging problem due to lack of human translated bilingual training data. In this paper, we report on an effort to port our existing English/Iraqi S2S system to the English/Farsi language pair in just 90 days, using only a small amount of training data. This effort included developing acoustic models for Farsi, domain-relevant language models for English and Farsi, and translation models for English-to-Farsi and Farsi-to-English. As part of this work, we developed two novel techniques for expanding the training data, including the reuse of data from different language pairs, and directed collection of new data. In an independent evaluation, the resulting system achieved the highest performance of all systems.
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1993–2009
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Raytheon BBN Technologies
Cambridge, MA, USA
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