Automatically measuring L2 speech fluency without the need of ASR: A proof-of-concept study with Japanese learners of French

Lionel Fontan, Maxime Le Coz, Sylvain Matthieu Julien Detey

研究成果: Conference article

1 引用 (Scopus)

抄録

This research work investigates the possibility of using automatic acoustic measures to assess speech fluency in the context of second language (L2) acquisition. To this end, three experts rated speech recordings of Japanese learners of French who were instructed to read aloud a 21-sentence-long text. A Forward-Backward Divergence Segmentation (FBDS) algorithm was used to segment speech recordings (sentences) into acoustically homogeneous units at a subphonemic scale. The FBDS processing results were used - along with more classic measures such as raw percentage of speech and length/standard deviation of silent pauses - to estimate speech rate and regularity of speech rate, while a formant tracking algorithm was used to estimate speech fluidity (i.e., quality of coarticulation). A step-by-step multiple linear regression was finally computed to predict the experts' mean fluency ratings. Results show that FBDS-derived measures, raw percentage of speech, and standard deviation of the first formant curve derivative can be combined together to calculate accurate estimates of speakers' fluency scores (R = .92; P < .001). As only low-level signal features were used in the study, the method could also be relevant for the assessment of speakers of other target languages, as well as for the assessment of disordered speech.

元の言語English
ページ(範囲)2544-2548
ページ数5
ジャーナルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2018-September
DOI
出版物ステータスPublished - 2018 1 1
イベント19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India
継続期間: 2018 9 22018 9 6

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Divergence
Segmentation
Standard deviation
Percentage
Estimate
Speech
Concepts
Fluency
Multiple Linear Regression
Fluidity
Linear regression
Acoustics
Regularity
Derivatives
Calculate
Derivative
Predict
Curve
Target
Unit

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

これを引用

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abstract = "This research work investigates the possibility of using automatic acoustic measures to assess speech fluency in the context of second language (L2) acquisition. To this end, three experts rated speech recordings of Japanese learners of French who were instructed to read aloud a 21-sentence-long text. A Forward-Backward Divergence Segmentation (FBDS) algorithm was used to segment speech recordings (sentences) into acoustically homogeneous units at a subphonemic scale. The FBDS processing results were used - along with more classic measures such as raw percentage of speech and length/standard deviation of silent pauses - to estimate speech rate and regularity of speech rate, while a formant tracking algorithm was used to estimate speech fluidity (i.e., quality of coarticulation). A step-by-step multiple linear regression was finally computed to predict the experts' mean fluency ratings. Results show that FBDS-derived measures, raw percentage of speech, and standard deviation of the first formant curve derivative can be combined together to calculate accurate estimates of speakers' fluency scores (R = .92; P < .001). As only low-level signal features were used in the study, the method could also be relevant for the assessment of speakers of other target languages, as well as for the assessment of disordered speech.",
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AB - This research work investigates the possibility of using automatic acoustic measures to assess speech fluency in the context of second language (L2) acquisition. To this end, three experts rated speech recordings of Japanese learners of French who were instructed to read aloud a 21-sentence-long text. A Forward-Backward Divergence Segmentation (FBDS) algorithm was used to segment speech recordings (sentences) into acoustically homogeneous units at a subphonemic scale. The FBDS processing results were used - along with more classic measures such as raw percentage of speech and length/standard deviation of silent pauses - to estimate speech rate and regularity of speech rate, while a formant tracking algorithm was used to estimate speech fluidity (i.e., quality of coarticulation). A step-by-step multiple linear regression was finally computed to predict the experts' mean fluency ratings. Results show that FBDS-derived measures, raw percentage of speech, and standard deviation of the first formant curve derivative can be combined together to calculate accurate estimates of speakers' fluency scores (R = .92; P < .001). As only low-level signal features were used in the study, the method could also be relevant for the assessment of speakers of other target languages, as well as for the assessment of disordered speech.

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KW - French

KW - Japanese

KW - Learner corpus

KW - Second language acquisition

KW - Speech fluency

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