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Conference Papers Year : 2022

Predicting speech fluency in children using automatic acoustic features

Lionel Fontan
Shinyoung Kim
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Sylvain Detey
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The present study aims at predicting the speech fluency of children using automatic acoustic measures derived from forward-backward divergence segmentation (FBDS). Thirteen Korean children were recorded while reading out loud a set of sentences. Three native-Korean speakers evaluated the fluency of each sentence on a five-point scale. A FBDS algorithm was used to segment speech recordings into sub-phonemic units and silent segments. In addition to the low-level acoustic features directly derived from FBDS segments, higher-level acoustic features were computed by clustering FBDS segments into pseudo-syllables and silent breaks. Both low-and higher-level features were used to predict average ratings of speech fluency, using a leave-one-speaker-out cross-validation scheme and three regression models: a multiple linear regression, a support vector regression, and a random-forest regressor. Highly accurate predictions were achieved, with average root-mean-square errors (RMSEs) as low as 0.3. Prediction accuracy did not significantly change as a function of regression model. Using higher-level features yielded lower RMSEs than using raw FBDS features. The results of a multiple linear regression using higher-level features (R 2 = 0.94) suggest that speech/silence ratio and pseudo-syllable rate are the two most important predictors of speech fluency.
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hal-03937320 , version 1 (19-01-2023)



Lionel Fontan, Shinyoung Kim, Verdiana De Fino, Sylvain Detey. Predicting speech fluency in children using automatic acoustic features. Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2022), Asia-Pacific Signal and Information Processing Association (APSIPA), Nov 2022, Chiang Mai, Thailand. pp.1086-1091, ⟨10.23919/APSIPAASC55919.2022.9979884⟩. ⟨hal-03937320⟩
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