31,595 research outputs found
Automatic Quality Estimation for ASR System Combination
Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%
Combatting electoral traces: the Dutch tempest discussion and beyond
In the Dutch e-voting debate, the crucial issue leading to the abandonment of all electronic voting machines was compromising radiation, or tempest. Other countries, however, do not seem to be bothered by this risk. In this paper, we use actor-network theory to analyse the socio-technical origins of the Dutch tempest issue in e-voting, and its consequences for e-voting beyond the Netherlands. We introduce the term electoral traces to denote any physical, digital or social evidence of a voter's choices in an election. From this perspective, we provide guidelines for risk analysis as well as an overview of countermeasures
Dependency relations as source context in phrase-based SMT
The Phrase-Based Statistical Machine Translation (PB-SMT) model has recently begun to include source context modeling, under the assumption that the proper lexical
choice of an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features such as words, parts-of-speech, and
supertags have been explored as effective source context in SMT. In this paper, we show that position-independent syntactic dependency relations of the head of a source phrase can be modeled as useful source context to improve target phrase selection and thereby improve overall performance of PB-SMT. On a Dutch—English translation task, by combining dependency relations and syntactic contextual features (part-of-speech), we achieved a 1.0 BLEU (Papineni et al., 2002) point improvement (3.1% relative) over the baseline
MATREX: the DCU MT system for WMT 2010
This paper describes the DCU machine translation system in the evaluation campaign of the Joint Fifth Workshop on Statistical Machine Translation and Metrics in ACL-2010. We describe the modular design of our multi-engine machine translation (MT) system with particular focus on the components used in this participation.
We participated in the English–Spanish and English–Czech translation tasks, in which we employed our multiengine
architecture to translate. We also participated in the system combination task which was carried out by the MBR
decoder and confusion network decoder
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