Difference between revisions of "Speech Recognition for Education"
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Wang et al. (2018) [https://www.researchgate.net/publication/336009443_Monitoring_the_performance_of_human_and_automated_scores_for_spoken_responses pdf] | Wang et al. (2018) [https://www.researchgate.net/publication/336009443_Monitoring_the_performance_of_human_and_automated_scores_for_spoken_responses pdf] | ||
*Automated scoring model for evaluating English spoken responses | *Automated scoring model for evaluating English spoken responses | ||
*SpeechRater gave a significantly lower score than human raters for German | *SpeechRater gave a significantly lower score than human raters for German students | ||
*SpeechRater | *SpeechRater gave higher scores to students from China than human raters, with H1-rater scores higher than mean | ||
Latest revision as of 05:09, 10 June 2022
Wang et al. (2018) pdf
- Automated scoring model for evaluating English spoken responses
- SpeechRater gave a significantly lower score than human raters for German students
- SpeechRater gave higher scores to students from China than human raters, with H1-rater scores higher than mean
Loukina & Buzick (2017) pdf
- a model (the SpeechRater) automatically scoring open-ended spoken responses for speakers with documented or suspected speech impairments
- SpeechRater was less accurate for test takers who were deferred for signs of speech impairment (ρ2 = .57) than test takers who were given accommodations for documented disabilities (ρ2 = .73)
Loukina et al. (2019) pdf
- Models providing automated speech scores on English language proficiency assessment
- L1-specific model trained on the speaker’s native language was the least fair, especially for Chinese, Japanese, and Korean speakers, but not for German speakers
- All models (Baseline, Fair feature subset, L1-specific) performed worse for Japanese speakers