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.
|Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
|Published - 2018
|19th Annual Conference of the International Speech Communication, INTERSPEECH 2018 - Hyderabad, India
継続期間: 2018 9月 2 → 2018 9月 6
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