An automatic pronunciation evaluation system using non-native teacher's speech model

Park, Hye-bin; Kim, Dong Heon; Joung, Jinoo

doi:10.7236/jiibc.2016.16.2.131

Cited by 3 publications

(2 citation statements)

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“…Adjusting parameters in hidden Markov models and improving the Viterbi algorithm, models with relatively low con dence in the recognition process are trimmed, in order to achieve higher recognition accuracy and shorter matching time. However, this method has a large space complexity in the back-end decoding algorithm, which restricts the scalability of the overall speech recognition system [8]. e acoustic model of the convolutional neural network was established by Qin et al for speech recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

English Speech Recognition and Pronunciation Quality Evaluation Model Based on Neural Network

Wang¹

2022

Scientific Programming

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An in-depth neural network-based approach is proposed to better develop an assessment model for English speech recognition and call quality assessment. By studying the structure of a deep nonlinear network, you can approximate complex functions, define distributed representations of input data, demonstrate a strong ability to learn important data set characteristics from some sample sets, and better simulate human brain analysis, and learning. The author uses in-depth learning technology to recognize English speech and has developed a speech recognition model with a deep belief network using the characteristics of the honey frequency centrum based on human hearing patterns. The test results show that examples include 210 machine and manual evaluations and 30 samples with first-grade differences. The overall compatibility level of the machine and human evaluation is 90.65%, and the adjacency consistency level is 90.65%. This is 100%, and the correlation coefficient is 0.798. We need to evaluate the quality of speech and pronunciation in English, which indicates a strong correlation between machine estimates and human estimates.

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Section: Literature Reviewmentioning

confidence: 99%

English Speech Recognition and Pronunciation Quality Evaluation Model Based on Neural Network

Wang¹

2022

Scientific Programming

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“…ASR technology, a software that uses consistent algorithms to analyze and recognize spoken language, has been widely used in various applications, including voice assistants, language translation, and speech-to-text (STT) transcription. The use of ASR technology in language education has been ongoing since the early 21st century (Park, 2017;Park et al, 2016). However, due to the lack of notable technological advancements at that time, it is difficult to compare it with recent research.…”

Section: Introductionmentioning

confidence: 99%

Digital enhancement of pronunciation assessment: Automated speech recognition and human raters*

Kim¹

2023

Phonetics Speech Sci.

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This study explores the potential of automated speech recognition (ASR) in assessing English learners' pronunciation. We employed ASR technology, acknowledged for its impartiality and consistent results, to analyze speech audio files, including synthesized speech, both native-like English and Korean-accented English, and speech recordings from a native English speaker. Through this analysis, we establish baseline values for the word error rate (WER). These were then compared with those obtained for human raters in perception experiments that assessed the speech productions of 30 first-year college students before and after taking a pronunciation course. Our sub-group analyses revealed positive training effects for Whisper, an ASR tool, and human raters, and identified distinct human rater strategies in different assessment aspects, such as proficiency, intelligibility, accuracy, and comprehensibility, that were not observed in ASR. Despite such challenges as recognizing accented speech traits, our findings suggest that digital tools such as ASR can streamline the pronunciation assessment process. With ongoing advancements in ASR technology, its potential as not only an assessment aid but also a self-directed learning tool for pronunciation feedback merits further exploration.

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Teaching Research on Automatic Evaluation System of Japanese Pronunciation Based on Artificial Intelligence

Lei

2022

2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City

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An automatic pronunciation evaluation system using non-native teacher's speech model

Cited by 3 publications

References 2 publications

English Speech Recognition and Pronunciation Quality Evaluation Model Based on Neural Network

English Speech Recognition and Pronunciation Quality Evaluation Model Based on Neural Network

Digital enhancement of pronunciation assessment: Automated speech recognition and human raters*

Teaching Research on Automatic Evaluation System of Japanese Pronunciation Based on Artificial Intelligence

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