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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.


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Proceedings ArticleDOI
19 Apr 1994
TL;DR: A tree-structured speaker clustering algorithm that employs successive branch selection in the speaker clustered tree rather than parameter training and hence achieves fast adaptation using only a small amount of training data.
Abstract: The paper proposes a tree-structured speaker clustering algorithm and discusses its application to fast speaker adaptation. By tracing the clustering tree from top to bottom, adaptation is performed step-by-step from global to local individuality of speech. This adaptation method employs successive branch selection in the speaker clustering tree rather than parameter training and hence achieves fast adaptation using only a small amount of training data. This speaker adaptation method was applied to a hidden Markov network (HMnet) and evaluated in Japanese phoneme and phrase recognition experiments, in which it significantly outperformed speaker-independent recognition methods. In the phrase recognition experiments, the method reduced the error rate by 26.6% using three phrase utterances (approximately 2.7 seconds). >

74 citations

Proceedings ArticleDOI
05 Jun 2000
TL;DR: An attempt to better model non-native lexical patterns is described, which are incorporated by applying context-independent phonetic confusion rules, whose probabilities are estimated from training data.
Abstract: The paper examines the recognition of non-native speech in JUPITER, a speaker-independent, spontaneous-speech conversational system. Because the non-native speech in this domain is limited and varied, speaker- and accent-specific methods are impractical. We therefore chose to model all of the non-native data with a single model. In particular, the paper describes an attempt to better model non-native lexical patterns. These patterns are incorporated by applying context-independent phonetic confusion rules, whose probabilities are estimated from training data. Using this approach, the word error rate on a non-native test set is reduced from 20.9% to 18.8%.

74 citations

Proceedings ArticleDOI
04 May 2014
TL;DR: This work proposes a novel second-order stochastic optimization algorithm based on analytic results showing that a non-zero mean of features is harmful for the optimization, and proves convergence of the algorithm in a convex setting.
Abstract: Deep neural networks are typically optimized with stochastic gradient descent (SGD). In this work, we propose a novel second-order stochastic optimization algorithm. The algorithm is based on analytic results showing that a non-zero mean of features is harmful for the optimization. We prove convergence of our algorithm in a convex setting. In our experiments we show that our proposed algorithm converges faster than SGD. Further, in contrast to earlier work, our algorithm allows for training models with a factorized structure from scratch. We found this structure to be very useful not only because it accelerates training and decoding, but also because it is a very effective means against overfitting. Combining our proposed optimization algorithm with this model structure, model size can be reduced by a factor of eight and still improvements in recognition error rate are obtained. Additional gains are obtained by improving the Newbob learning rate strategy.

74 citations

Journal ArticleDOI
TL;DR: The overall error rate in laboratory medicine was found to be 20.0%, which indicates that, also on the clinical side, error reduction is desirable, especially in the requesting of laboratory investigation.

74 citations

Journal ArticleDOI
Tara N. Sainath1, Bhuvana Ramabhadran1, Michael Picheny1, David Nahamoo1, Dimitri Kanevsky1 
TL;DR: This paper combines the advantages of using both small and large vocabulary tasks by taking well-established techniques used in LVCSR systems and applying them on TIMIT to establish a new baseline, creating a novel set of exemplar-based sparse representation (SR) features.
Abstract: The use of exemplar-based methods, such as support vector machines (SVMs), k-nearest neighbors (kNNs) and sparse representations (SRs), in speech recognition has thus far been limited. Exemplar-based techniques utilize information about individual training examples and are computationally expensive, making it particularly difficult to investigate these methods on large-vocabulary continuous speech recognition (LVCSR) tasks. While research in LVCSR provides a good testbed to tackle real-world speech recognition problems, research in this area suffers from two main drawbacks. First, the overall complexity of an LVCSR system makes error analysis quite difficult. Second, exploring new research ideas on LVCSR tasks involves training and testing state-of-the-art LVCSR systems, which can render a large turnaround time. This makes a small vocabulary task such as TIMIT more appealing. TIMIT provides a phonetically rich and hand-labeled corpus that allows easy insight into new algorithms. However, research ideas explored for small vocabulary tasks do not always provide gains on LVCSR systems. In this paper, we combine the advantages of using both small and large vocabulary tasks by taking well-established techniques used in LVCSR systems and applying them on TIMIT to establish a new baseline. We then utilize these existing LVCSR techniques in creating a novel set of exemplar-based sparse representation (SR) features. Using these existing LVCSR techniques, we achieve a phonetic error rate (PER) of 19.4% on the TIMIT task. The additional use of SR features reduce the PER to 18.6%. We then explore applying the SR features to a large vocabulary Broadcast News task, where we achieve a 0.3% absolute reduction in word error rate (WER).

74 citations


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