Classification of Regional Accent Using Speech Rhythm Metrics

Droua-Hamdani, Ghania

doi:10.1007/978-3-030-26061-3_8

Cited by 10 publications

(1 citation statement)

References 6 publications

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“…Prosodic features, also known as super segment features, mainly refer to the speed, rhythm and intonation formed by dynamic patterns of related parameters such as pitch, intensity and duration. This paper adopts speech speed evaluation on speech duration, rhythm evaluation on short-term energy and pairwise variation index (Distinct Pairwise Variability Index, d PV), and evaluation and feedback guidance on English pronunciation quality on pitch evaluation of fundamental frequency (Droua-Hamdani, 2019). ...…”

Section: Multiparameter Pronunciation Quality Evaluationmentioning

confidence: 99%

The impact of student learning aids on deep learning and mobile platform on learning behavior

Fan

Liu

2022

LHT

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PurposeDeep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students’ learning behavior.Design/methodology/approachDL technology is used to design a speech evaluation system.FindingsThe experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency.Originality/valueThe results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.

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Section: Multiparameter Pronunciation Quality Evaluationmentioning

confidence: 99%