Automatic pronunciation scoring for language instruction

Franco, Horacio; Neumeyer, Leonardo; Kim, Yoon; Ronen, Orith

doi:10.1109/icassp.1997.596227

Cited by 137 publications

(93 citation statements)

References 2 publications

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“…So approximations are used, such as substituting a maximum for the summation, etc. Formula 2 is the general framework for pronunciation diagnosis (Witt and Young, 1997;Witt and Young, 2000) and pronunciation assessment (Witt and Young, 2000;Franco et al, 1997;Bernstein et al, 2010). Various authors use different approximations to suit the particulars of their data and their applications.…”

Section: Spectral Modelingmentioning

confidence: 99%

Automatic Assessment of the Speech of Young English Learners

Cheng¹,

D'Antilio²,

Chen³

et al. 2014

Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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This paper introduces some of the research behind automatic scoring of the speaking part of the Arizona English Language Learner Assessment, a large-scale test now operational for students in Arizona. Approximately 70% of the students tested are in the range 4-11 years old. We cover the methods used to assess spoken responses automatically, considering both what the student says and the way in which the student speaks. We also provide evidence for the validity of machine scores. The assessments include 10 open-ended item types. For 9 of the 10 open item types, machine scoring performed at a similar level or better than human scoring at the item-type level. At the participant level, correlation coefficients between machine overall scores and average human overall scores were: Kindergarten: 0.88; Grades 1-2: 0.90; Grades 3-5: 0.94; Grades 6-8: 0.95; Grades 9-12: 0.93. The average correlation coefficient was 0.92. We include a note on implementing a detector to catch problematic test performances.

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Section: Spectral Modelingmentioning

confidence: 99%

Automatic Assessment of the Speech of Young English Learners

Cheng¹,

D'Antilio²,

Chen³

et al. 2014

Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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“…The disadvantage of these methods is that they are text-dependent, so they only work for the utterances with the same text of the native recordings, but can not be used on other utterances. Neumeyer et al presented a textindependent pronunciation assessment framework [11] in 1996, then they improved the method by using the posterior probabilities instead of decoding log-likelihood [12], [13]. Witt et al combined the advantages of these works and presented the GOP method.…”

Section: Introductionmentioning

confidence: 99%

A Novel Discriminative Method for Pronunciation Quality Assessment

Zhang¹,

Pan²,

Dong³

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we presented a novel method for automatic pronunciation quality assessment. Unlike the popular "Goodness of Pronunciation" (GOP) method, this method does not map the decoding confidence into pronunciation quality score, but differentiates the different pronunciation quality utterances directly. In this method, the student's utterance need to be decoded for two times. The first-time decoding was for getting the time points of each phone of the utterance by a forced alignment using a conventional trained acoustic model (AM). The second-time decoding was for differentiating the pronunciation quality for each triphone using a specially trained AM, where the triphones in different pronunciation qualities were trained as different units, and the model was trained in discriminative method to ensure the model has the best discrimination among the triphones whose names were same but pronunciation quality scores were different. The decoding network in the second-time decoding included different pronunciation quality triphones, so the phone-level scores can be obtained from the decoding result directly. The phone-level scores were combined into the sentence-level scores using maximum entropy criterion. The experimental results shows that the scoring performance was increased significantly compared to the GOP method, especially in sentence-level.

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“…In China, it would be especially useful to develop a CALL system with remedial instructions, since a large number of Chinese learners have no chance to talk with the native speakers to practice and then correct their pronunciations. However, most previous studies on CALL systems were designed to give a pronunciation measurement for nonnative speakers [1,7,3,9]. These methods incorporating with speech recognition techniques focus on accessing non-native speakers' pronunciation quality in a good or poor level.…”

Section: Introductionmentioning

confidence: 99%

Mispronunciation detection based on cross-language phonological comparisons

Wang

Feng

Meng

2008

2008 International Conference on Audio, Language and Image Processing

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This paper presents a method using speech recognition with linguistic constraints to detect the mispronunciations made by Cantonese learners of English. The predicted pronunciation errors have been derived from cross-language phonological comparisons, which are used to generate the erroneous pronunciation variations in a lexicon. The acoustic models are trained with native speakers' speech and used for recognizing the phone sequences, given the orthographic transcriptions. The experiments have examined that the agreement between automatic mispronunciation detection and human judges is over 84% for 21 Cantonese speakers.

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Automatic pronunciation scoring for language instruction

Cited by 137 publications

References 2 publications

Automatic Assessment of the Speech of Young English Learners

Automatic Assessment of the Speech of Young English Learners

A Novel Discriminative Method for Pronunciation Quality Assessment

Mispronunciation detection based on cross-language phonological comparisons

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