Information encoding and transmission profiles of first-language (L1) and second-language (L2) speech

Bradlow, Ann R.

doi:10.1017/s1366728921000717

Cited by 3 publications

(1 citation statement)

References 64 publications

(100 reference statements)

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“…The rapid development of educational data mining and educational intelligence technology has brought new opportunities for TBA, including CDA. Consequently, the importance of classroom discourse in secondary school education has been greatly emphasized 13 , 14 . Theoretical analysis and empirical tests suggest that classroom discourse is directly related to the dissemination effect of teaching information.…”

Section: Construction Of the Cda Framework In Secondary School Educationmentioning

confidence: 99%

The use of deep learning integrating image recognition in language analysis technology in secondary school education

Chu,

Liu,

Zhai

et al. 2024

Sci Rep

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This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse’s speaking rate, speech intelligibility, average sentence length, and content similarity are −0.56, −0.71, −0.71, and −0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.

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Section: Construction Of the Cda Framework In Secondary School Educationmentioning

confidence: 99%

The use of deep learning integrating image recognition in language analysis technology in secondary school education

Chu,

Liu,

Zhai

et al. 2024

Sci Rep

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Listening to accents: Comprehensibility, accentedness and intelligibility of native and non-native English speech

Verbeke,

Simon

2023

Lingua

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Generalized perceptual adaptation to second-language speech: Variability, similarity, and intelligibility

Bradlow,

Bassard,

Paller

2023

The Journal of the Acoustical Society of America

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Recent work on perceptual learning for speech has suggested that while high-variability training typically results in generalization, low-variability exposure can sometimes be sufficient for cross-talker generalization. We tested predictions of a similarity-based account, according to which, generalization depends on training-test talker similarity rather than on exposure to variability. We compared perceptual adaptation to second-language (L2) speech following single- or multiple-talker training with a round-robin design in which four L2 English talkers from four different first-language (L1) backgrounds served as both training and test talkers. After exposure to 60 L2 English sentences in one training session, cross-talker/cross-accent generalization was possible (but not guaranteed) following either multiple- or single-talker training with variation across training-test talker pairings. Contrary to predictions of the similarity-based account, adaptation was not consistently better for identical than for mismatched training-test talker pairings, and generalization patterns were asymmetrical across training-test talker pairs. Acoustic analyses also revealed a dissociation between phonetic similarity and cross-talker/cross-accent generalization. Notably, variation in adaptation and generalization related to variation in training phase intelligibility. Together with prior evidence, these data suggest that perceptual learning for speech may benefit from some combination of exposure to talker variability, training-test similarity, and high training phase intelligibility.

show abstract

Information encoding and transmission profiles of first-language (L1) and second-language (L2) speech

Cited by 3 publications

References 64 publications

The use of deep learning integrating image recognition in language analysis technology in secondary school education

The use of deep learning integrating image recognition in language analysis technology in secondary school education

Listening to accents: Comprehensibility, accentedness and intelligibility of native and non-native English speech

Generalized perceptual adaptation to second-language speech: Variability, similarity, and intelligibility

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