Prediction of L2 speech proficiency based on multi-level linguistic features

Verdiana De Fino, Lionel Fontan, Julien Pinquier, Isabelle Ferrané, Sylvain Detey

研究成果: Conference article査読

抄録

This study investigates the possibility to use automatic, multi-level features for the prediction of L2 speech proficiency. The method was applied on a corpus containing audio recordings and transcripts for 38 Japanese learners of French who participated in a semi-spontaneous oral production task. Each learner's speech proficiency level was assessed by three experienced French teachers. Audio recordings were processed to extract features related to the pronunciation skills and phonetic fluency of the learners, while the transcripts were used to measure their lexical, syntactic, and discursive abilities in French. A Lasso regression using a leave-one-out cross-validation procedure was used to select relevant features and to accurately predict speech proficiency scores. The results show that five features related to the phonetic fluency (speech rate), lexical abilities (lexical density), discourse planning and elaboration skills (number of hesitation and false starts, mean utterance length) of the learners can be used to predict speech proficiency ratings (r = 0.71, mean absolute error on a 5-point scale: 0.53).

本文言語English
ページ(範囲)4043-4047
ページ数5
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2022-September
DOI
出版ステータスPublished - 2022
イベント23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Korea, Republic of
継続期間: 2022 9月 182022 9月 22

ASJC Scopus subject areas

  • 言語および言語学
  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • モデリングとシミュレーション

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