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Word error rate

About: Word error rate is a research topic. Over the lifetime, 11939 publications have been published within this topic receiving 298031 citations.


Papers
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Journal ArticleDOI
Wu Chou1
01 Aug 2000
TL;DR: A discriminant function-based minimum recognition error rate pattern recognition approach is described and studied for various applications in speech processing, and issues in this new classifier design paradigm are discussed and various extensions of this approach are presented.
Abstract: A discriminant function-based minimum recognition error rate pattern recognition approach is described and studied for various applications in speech processing. This approach departs from the conventional paradigm, which links a classification/recognition task to the problem of distribution estimation. Instead, it takes a discriminant function based statistical pattern recognition approach. The suitability of this approach for classification error rate minimization is established through a special loss function. It is meaningful even when the model correctness assumption is known to be not valid. We study the theoretical basis of this approach and compare it with various criteria used in speech recognition. We differentiate the method of classifier design by way of distribution estimation and the discriminant function methods of minimizing classification error rate, based on the fact that in many realistic applications, such as speech recognition, the true distribution form of the source is rarely known precisely, and without model correctness assumption, the classical optimality theory of the distribution estimation approach cannot be applied directly. We discuss issues in this new classifier design paradigm and present various extensions of this approach to classifier design applications in speech processing.

76 citations

Patent
Matthew W. Hartley1, David E. Reich1
16 May 2001
TL;DR: In this paper, a method for performing speech recognition can include the steps of providing a grammar including entries comprising a parent word and a pseudo word being substantially phonetically equivalent to the parent word.
Abstract: A method for performing speech recognition can include the steps of providing a grammar including entries comprising a parent word and a pseudo word being substantially phonetically equivalent to the parent word. The grammar can provide a translation from the pseudo word to the parent word. The parent word can be received as speech and the speech can be compared to the grammar entries. Additionally, the speech can be matched to the pseudo word and the pseudo word can be translated to the parent word.

76 citations

Patent
13 May 2002
TL;DR: A linear transformation of parallel multiple input, multiple output (MIMO) encoded streams; also, space-time diversity and asymmetrical symbol mapping of parallel streams are discussed in this paper.
Abstract: A linear transformation of parallel multiple input, multiple output (MIMO) encoded streams; also, space-time diversity and asymmetrical symbol mapping of parallel streams. Separately or together, these improve error rate performance as well as system throughput. Preferred embodiments include CDMA wireless systems with multiple antennas.

76 citations

Journal ArticleDOI
TL;DR: This study examines several key issues in system combination for the word sense disambiguation task, ranging from algorithmic structure to parameter estimation, and demonstrates that the combination system obtains a significantly lower error rate than other systems participating in the SENSEVAL2 exercise.
Abstract: Classifier combination is an effective and broadly useful method of improving system performance. This article investigates in depth a large number of both well-established and novel classifier combination approaches for the word sense disambiguation task, studied over a diverse classifier pool which includes feature-enhanced Naive Bayes, Cosine, Decision List, Transformation-based Learning and MMVC classifiers. Each classifier has access to the same rich feature space, comprised of distance weighted bag-of-lemmas, local ngram context and specific syntactic relations, such as Verb-Object and Noun-Modifier. This study examines several key issues in system combination for the word sense disambiguation task, ranging from algorithmic structure to parameter estimation. Experiments using the standard SENSEVAL2 lexical-sample data sets in four languages (English, Spanish, Swedish and Basque) demonstrate that the combination system obtains a significantly lower error rate when compared with other systems participating in the SENSEVAL2 exercise, yielding state-of-the-art performance on these data sets.

75 citations

Proceedings ArticleDOI
27 May 2003
TL;DR: A generative probabilistic optical character recognition model is introduced that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
Abstract: In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system. The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks. We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.

75 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023271
2022562
2021640
2020643
2019633
2018528