Author: Dimitra Vergyri

We summarize recent progress on SRI’s IraqComm™ IraqiArabic-English two-way speech-to-speech translation system.

We describe the large vocabulary automatic speech recognition system developed for Modern Standard Arabic used for the 2007 GALE evaluation as part of the speech translation system.

We address the problem of retrieving out-of-vocabulary (OOV) words/queries from audio archives for spoken term detection (STD) task. In this work, we employ hybrid recognition systems which contain both words and subword units (graphones) to generate hybrid lattice indexes.

We propose a new method for detecting out-of-vocabulary (OOV) words for large vocabulary continuous speech recognition (LVCSR) systems. Our method is based on performing a joint alignment between independently generated word and phone lattices, where the word-lattice is aligned via a recognition lexicon.

This paper describes the IraqComm translation system that mediates and translates spontaneous conversations between an English speaker and a speaker of colloquial Iraqi Arabic.

This paper describes the system developed jointly at SRI and OGI for participation in the 2006 NIST Spoken Term Detection (STD) evaluation.

In this paper, we investigate improvements in Arabic language modeling by developing various morphology-based language models. We present four different approaches to morphology-based language modeling, including a novel technique called factored language models.

In this paper we investigate how portable such features are across domains and languages. We show that even without retraining, English-trained MLP features can provide a significant boost to recognition accuracy in new domains within the same language, as well as in entirely different languages such as Mandarin and Arabic.

We present a number of challenges and solutions that have arisen in the development of a speech translation system for American English and Pashto, highlighting those specific to a very low resource language.

In this paper, we describe the development of a large-vocabulary speech recognition system for Levantine Arabic, which was a new dialectal recognition task for our existing system. We discuss the dialect-specific modeling choices and investigate to what extent techniques previously tested on other languages are portable to the present task.

In this paper we investigate the use of morphology-based language models at different stages in a speech recognition system for conversational Arabic.