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The region-dependent transform (RDT) is a feature extraction method for speech recognition that employs the Minimum Phoneme Error (MPE) criterion to optimize a set of feature transforms , each concentrating on a region of the acoustic space. Previous results have shown that RDT gives significant recognition-error reduction in a large vocabulary(More)
Discriminatively trained feature transforms such as MPE-HLDA, fMPE and MMI-SPLICE have been shown to be effective in reducing recognition errors in today's state-of-the-art speech recognition systems. This paper introduces the concept of region dependent linear transform (RDLT), which unifies the above three types of feature transforms and provides a(More)
Current statistical machine translation (SMT) systems are trained on sentence-aligned and word-aligned parallel text collected from various sources. Translation model parameters are estimated from the word alignments, and the quality of the translations on a given test set depends on the parameter estimates. There are at least two factors affecting the(More)
This paper describes the speech activity detection (SAD) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We present two approaches to SAD, one based on Gaussian(More)
Confusion network decoding has been the most successful approach in combining outputs from multiple machine translation (MT) systems in the recent DARPA GALE and NIST Open MT evaluations. Due to the varying word order between outputs from different MT systems, the hypothesis alignment presents the biggest challenge in confusion network decoding. This paper(More)
In this paper we introduce a discriminative feature analysis method that seeks to minimize phoneme errors in lattice-based training frameworks. This technique, referred to as Minimum Phoneme Error Heteroscedastic Linear Discriminant Analysis (MPE-HLDA), is shown to be more robust than traditional LDA methods in high dimensional spaces, and easy to(More)
BBN submitted system combination outputs for CzechEnglish language pairs. All combinations were based on confusion network decoding. An incremental hypothesis alignment algorithm with flexible matching was used to build the networks. The bi-gram decoding weights for the single source language translations were tuned directly to maximize the BLEU score of(More)
Current methods of using lexical features in machine translation have difficulty in scaling up to realistic MT tasks due to a prohibitively large number of parameters involved. In this paper, we propose methods of using new linguistic and con-textual features that do not suffer from this problem and apply them in a state-of-the-art hierarchical MT system.(More)