Automated analysis of newborn cry: relationships between melodic shapes and native language. (August 2019)

• Record Type: Journal Article
• Authors: Manfredi, C.; Viellevoye, R.; Orlandi, S.; Torres-García, A.; Pieraccini, G.; Reyes-García, C.A.
• Is Part Of: Biomedical signal processing and control. Volume 53(2019)
• Publication Date: 2019-08
• Subjects: Newborn cry melody -- Mother language -- Automated acoustical analysis -- Classification algorithms; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2019.101561
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: First results are presented concerning the automated mother language assessment of healthy full term newborns. About 7.500 cry units coming from French, Arabic and Italian mother-tongue newborns are analyzed. Acoustical parameters and 12 different melodic shapes are computed with the BioVoice software tool. Classification is performed with Random Forest and 4 neuro-fuzzy classifiers. Results show up to 95% differences among the three languages. Abstract: Recent research studies have shown that since the last trimester of pregnancy human fetuses are able to listen to and possibly memorize auditory stimuli from the external world, both as music and language are concerned. In particular, they exhibit a specific sensitivity to prosodic features such as melody, intensity, and rhythm that are essential for an infant to learn and develop the native language. This paper presents first results concerning the automated mother language identification of a set of about 7500 cry units coming from French, Arabic and Italian mother-tongue healthy full term newborns. Acoustical parameters and 12 different melodic shapes are computed with the BioVoice software tool and their classification is performed with Random Forest and 4 neuro-fuzzy classifiers. Results show up to 95% differences among the three languages.