Combination of machine scores for automatic grading of pronunciation quality

Franco, Horacio; Neumeyer, Leonardo; Digalakis, Vassilios; Ronen, Orith

doi:10.1016/s0167-6393(99)00045-x

Cited by 101 publications

(62 citation statements)

References 10 publications

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“…(2) The modern educational technology Active learning highlights the main position of students in the learning process while the teachers only play a role of organization and guidance in the process so as to promote students to construct knowledge and process information in a better way [11]. Textbooks are no longer the only learning content as a variety of modern technology in the teaching process are the cognitive tools for students to take the initiative to learn.…”

Section: B Factors Affecting Active Learningmentioning

confidence: 99%

Research and Software Development of Active Learning Mode for College Students Based on Computer Network

2016

Int. J. Emerg. Technol. Learn.

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Abstract-With the development of information technology, especially the fast development of Internet technology, the network technology has expanded to all walks of life. Using Internet to acquire knowledge has become an important means for us. The paper sums up the current status of foreign active learning modes and the problems existing in computer network environment by referring to the relevant literature. What is more, it also further clarifies the basic concepts and connotations of active learning in the computer network environment and active learning itself by researching on its relevant theoretical basis and influential factors as well. "A new active learning mode in computer network environment" is constructed based on the analysis of the relevant factors of active learning in the computer network environment, taking college students as the studying object and the college English course as an example. According to this model, the active learning platform of college English network is designed and developed. The research conclusion is not only theoretically based, but also founded on the concrete practice and the application. Therefore, it is endowed with maneuverability so as to provide a research example for the general teachers.Index Terms-Internet technology; active learning mode; computer network environment; platform of college English

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Section: B Factors Affecting Active Learningmentioning

confidence: 99%

Research and Software Development of Active Learning Mode for College Students Based on Computer Network

2016

Int. J. Emerg. Technol. Learn.

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“…Franco et al (2000aFranco et al ( , 2000b) present a system for automatic evaluation of the pronunciation quality of both native and nonnative speakers of English on the phone level and the sentence level (EduSpeak).…”

Section: Related Workmentioning

confidence: 99%

Automatic scoring of non-native spontaneous speech in tests of spoken English

Zechner

Higgins

et al. 2009

Speech Communication

203

149

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“…An early example is Pols et al (1973), in which the automatic classification of Dutch monophthongs was investigated. More recently, research specifically targeted toward automatic pronunciation quality measures that can be employed in ASR-based CAPT systems has focused on confidence scoring (Witt, 1999;Franco et al, 2000;Yoon et al, 2010;Wei et al, 2009;van Doremalen et al, 2009) using ASR-based techniques. This type of research has shown that pronunciation errors can be accurately detected to a certain extent (Witt, 1999;Franco et al, 2000;Cucchiarini et al, 2009;Wei et al, 2009) and that difficulties may arise when it comes to identifying pronunciation errors that are based on subtle acoustic differences .…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we argue that the error patterns that can derive from such interference are factors that can be utilized in the computation of automatic measures of pronunciation quality to improve their performance. So far various measures of pronunciation quality have been proposed (Witt, 1999;Franco et al, 2000) that manage to identify relatively conspicuous errors. However, in our own research, we found that the widely used GOP scoring algorithm (Witt, 1999) has difficulties in detecting subtle errors in target phonemes with acoustically close "neighboring" phonemes .…”

Section: Introductionmentioning

confidence: 99%

Automatic pronunciation error detection in non-native speech: The case of vowel errors in Dutch

Doremalen

Cucchiarini

Strik

2013

The Journal of the Acoustical Society of America

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This research is aimed at analyzing and improving automatic pronunciation error detection in a second language. Dutch vowels spoken by adult non-native learners of Dutch are used as a test case. A first study on Dutch pronunciation by L2 learners with different L1s revealed that vowel pronunciation errors are relatively frequent and often concern subtle acoustic differences between the realization and the target sound. In a second study automatic pronunciation error detection experiments were conducted to compare existing measures to a metric that takes account of the error patterns observed to capture relevant acoustic differences. The results of the two studies do indeed show that error patterns bear information that can be usefully employed in weighted automatic measures of pronunciation quality. In addition, it appears that combining such a weighted metric with existing measures improves the equal error rate by 6.1 percentage points from 0.297, for the Goodness of Pronunciation (GOP) algorithm, to 0.236. V

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Combination of machine scores for automatic grading of pronunciation quality

Cited by 101 publications

References 10 publications

Research and Software Development of Active Learning Mode for College Students Based on Computer Network

Research and Software Development of Active Learning Mode for College Students Based on Computer Network

Automatic scoring of non-native spontaneous speech in tests of spoken English

Automatic pronunciation error detection in non-native speech: The case of vowel errors in Dutch

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