Publications
The MIT-LL/AFRL IWSLT-2011 MT System
Summary
Summary
This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2011 evaluation campaign. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Arabic to English and English to French TED-talk...
The MIT-LL/AFRL IWSLT-2010 MT system
Summary
Summary
This paper describes the MIT-LUAFRL statistical MT system and the improvements that were developed during the IWSLT 2010 evaluation campaign. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance on the Arabic and Turkish to English translation tasks. We...
Advocate: a distributed architecture for speech-to-speech translation
Summary
Summary
Advocate is a set of communications application programming interfaces and service wrappers that serve as a framework for creating complex and scalable real-time software applications from component processing algorithms. Advocate can be used for a variety of distributed processing applications, but was initially designed to use existing speech processing and...
Advocate: a distributed voice-oriented computing architecture
Summary
Summary
Advocate is a lightweight and easy-to-use computing architecture that supports real-time, voice-oriented computing. It is designed to allow the combination of multiple speech and language processing components to create cohesive distributed applications. It is scalable, supporting local processing of all NLP/speech components when sufficient processing resources are available to one...
Low-resource speech translation of Urdu to English using semi-supervised part-of-speech tagging and transliteration
Summary
Summary
This paper describes the construction of ASR and MT systems for translation of speech from Urdu into English. As both Urdu pronunciation lexicons and Urdu-English bitexts are sparse, we employ several techniques that make use of semi-supervised annotation to improve ASR and MT training. Specifically, we describe 1) the construction...