Predicting developmental language disorders using artificial intelligence and a speech data analysis tool

References

• Alnahdi, G. H., & Schwab, S. (2020). Psychometric properties of the arabic version of the behavioral intention to interact with peers with intellectual disability scale. Frontiers in Psychology, 11, 1212. https://doi.org/10.3389/fpsyg.2020.01212
   PubMed Web of Science ®Google Scholar
• American Psychiatric Association, A. P. & American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, DSM-5 (5th ed.). Arlington, VA: American Psychiatric Association.
   Google Scholar
• Arosio, F., Branchini, C., Barbieri, L., & Guasti, M. T. (2014). Failure to produce direct object clitic pronouns as a clinical marker of sli in school-aged Italian speaking children. Clinical Linguistics & Phonetics, 28(9), 639–663. https://doi.org/10.3109/02699206.2013.877081
   PubMed Web of Science ®Google Scholar
• Aschersleben, G. (2002). Temporal control of movements in sensorimotor synchronization. Brain and Cognition, 48(1), 66–79. https://doi.org/10.1006/brcg.2001.1304
   PubMed Web of Science ®Google Scholar
• Atherton, R. P., Chrobak, Q. M., Rauscher, F. H., Karst, A. T., Hanson, M. D., Steinert, S. W., & Bowe, K. L. (2018). Shared processing of language and music. Experimental Psychology, 65(1), 40–48. https://doi.org/10.1027/1618-3169/a000388
   PubMed Web of Science ®Google Scholar
• Bates, D. S. (1996). Jumps and stochastic volatility: Exchange rate processes implicit in deutsche mark options. The Review of Financial Studies, 9(1), 69–107. https://doi.org/10.1093/rfs/9.1.69
   Web of Science ®Google Scholar
• Beccaluva, E. A., Riccardi, F., Bettelli, G., Moskwa, L., DiIorio, M., DiGioia, F. R., Pasqua, E., Arosio, F., Guasti, M. T. & Garzotto, F. (2022). Grasping the grammar with moovy: A tangible user interface to train linguistic skills in children. In Chi conference on human factors in computing systems extended abstracts, 30th April- 5th May 2022, New Orleans, USA, 32, 1–7. https://doi.org/10.1145/3491101.3519733.
   Google Scholar
• Beccaluva, E., Riccardi, F., Gianotti, M., Barbieri, J., & Garzotto, F. (2022). Vic—a tangible user interface to train memory skills in children with intellectual disability. International Journal of Child-Computer Interaction, 32, 100376. https://doi.org/10.1016/j.ijcci.2021.100376
   Google Scholar
• Bertelli, B., Moniga, S., Muraro, I., Zanella, G., & Pettenati, P. (2017). Prova di comprensione grammaticale con oggetti (pcgo). dati preliminari su bambini tra i 2 e 4 anni di età. Psicologia Clinica dello Sviluppo, 21(1), 79–104.
   Google Scholar
• Besson, M., & Schön, D. (2001). Comparison between language and music. Annals of the New York Academy of Sciences, 930(1), 232–258. https://doi.org/10.1111/j.1749-6632.2001.tb05736.x
   PubMed Web of Science ®Google Scholar
• Bishop, D. V. (2014). Ten Questions about terminology for children with unexplained language problems. International Journal of Language & Communication Disorders, 49(4), 381–415. https://doi.org/10.1111/1460-6984.12101
   PubMed Web of Science ®Google Scholar
• Black, L. I., Vahratian, A., & Hoffman, H. J. (2015). Communication disorders and use of intervention services among children aged 3-17 years: United states, 2012. US Department of Health and Human Services, Centers for Disease Control and.
   Google Scholar
• Boerma, T., & Blom, E. (2017). Assessment of bilingual children: What if testing both languages is not possible? Journal of Communication Disorders, 66, 65–76. https://doi.org/10.1016/j.jcomdis.2017.04.001
   PubMed Web of Science ®Google Scholar
• Bonillo, C., Baldassarri, S., Marco, J., & Cerezo, E. (2019). Tackling developmental delays with therapeutic activities based on tangible tabletops. Universal Access in the Information Society, 18(1), 31–47. https://doi.org/10.1007/s10209-017-0582-4
   Web of Science ®Google Scholar
• Bowerman, M., Levinson, S. C., Bowerman, M., Levinson, S. (2001). Language acquisition and conceptual development (No. 3). Cambridge University Press. https://doi.org/10.1017/CBO9780511620669
   Google Scholar
• Brewer, J. (2004). Web accessibility highlights and trends. In Proceedings of the 2004 international cross-disciplinary workshop on web accessibility (w4a), 17-22 May 2004, New York, NY, USA (pp. 51–55).
   Google Scholar
• Brophy, P., & Craven, J. (2007). Web accessibility. Library Trends, 55(4), 950–972. https://doi.org/10.1353/lib.2007.0029
   Web of Science ®Google Scholar
• Campbell, T., Dollaghan, C., Needleman, H., & Janosky, J. (1997). Reducing bias in language assessment: Processing-dependent measures. Journal of Speech, Language, & Hearing Research, 40(3), 519–525. https://doi.org/10.1044/jslhr.4003.519
   PubMed Web of Science ®Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
   Web of Science ®Google Scholar
• Chen, J. L., Penhune, V. B., & Zatorre, R. J. (2008). Moving on time: Brain network for auditory-motor synchronization is modulated by rhythm complexity and musical training. Journal of Cognitive Neuroscience, 20(2), 226–239. https://doi.org/10.1162/jocn.2008.20018
   PubMed Web of Science ®Google Scholar
• Chen, Y., Zhu, G., Liu, D., Liu, Y., Yuan, T., Zhang, X., Jiang, Y., Du, T., & Zhang, J. (2020). Brain morphological changes in hypokinetic dysarthria of parkinson’s disease and use of machine learning to predict severity. CNS Neuroscience & Therapeutics, 26(7), 711–719. https://doi.org/10.1111/cns.13304
   PubMed Web of Science ®Google Scholar
• Chiat, S. (2015). Nonword repetition. Methods for Assessing Multilingual Children: Disentangling Bilingualism from Language Impairment, 13(1), 125–150. https://doi.org/10.21832/9781783093137-008
   Google Scholar
• Chilosi, A. M., & Cipriani, P. (1995). Tcgb: Test di comprensione grammaticale per bambini: manuale. Edizioni del Cerro.
   Google Scholar
• Chilosi, A. M., Pfanner, L., Pecini, C., Salvadorini, R., Casalini, C., Brizzolara, D., & Cipriani, P. (2019). Which linguistic measures distinguish transient from persistent language problems in late talkers from 2 to 4 years? a study on Italian speaking children. Research in Developmental Disabilities, 89, 59–68. https://doi.org/10.1016/j.ridd.2019.03.005
   PubMed Web of Science ®Google Scholar
• Cioni, G., & Sgandurra, G. (2013). Normal psychomotor development. Handbook of Clinical Neurology, 111, 3–15. https://doi.org/10.1016/B978-0-444-52891-9.00001-4
   PubMedGoogle Scholar
• Clark, E. V. (2009). First language acquisition. Cambridge University Press.
   Google Scholar
• Clark, E. V., & Casillas, M. (2015). First language acquisition. In K. Allan (Ed.) The routledge handbook of linguistics (pp. 327–344). Routledge. https://doi.org/10.4324/9781315718453
   Google Scholar
• Clegg, J., Hollis, C., Mawhood, L., & Rutter, M. (2005). Developmental language disorders - a follow-up in later adult life. Cognitive, language and psychosocial outcomes. Journal of Child Psychology and Psychiatry, 46(2), 128–149. https://doi.org/10.1111/j.1469-7610.2004.00342.x
   PubMed Web of Science ®Google Scholar
• Consortium, W. W. W., et al. (1998). Web accessibility initiative (wai). http://www.w3.org/WAI.
   Google Scholar
• Contento, S., Bellocchi, S., & Bonifacci, P. (2013). Babil. Prove per la valutazione delle competenze verbali e non verbali in bambini bilingui.
   Google Scholar
• Corriveau, K. H., & Goswami, U. (2009). Rhythmic motor entrainment in children with speech and language impairments: Tapping to the beat. Cortex, 45(1), 119–130. https://doi.org/10.1016/j.cortex.2007.09.008
   PubMed Web of Science ®Google Scholar
• Corriveau, K., Pasquini, E., & Goswami, U. (2007). Basic auditory processing skills and specific language impairment: A new look at an old hypothesis. Journal of Speech, Language, & Hearing Research, 50(3), 647–666. https://doi.org/10.1044/1092-4388(2007/046)
   PubMed Web of Science ®Google Scholar
• de Boysson-Bardies, B., Sagart, L., & Durand, C. (1984). Discernible differences in the babbling of infants according to target language. Journal of Child Language, 11(1), 1–15. https://doi.org/10.1017/S0305000900005559
   PubMed Web of Science ®Google Scholar
• de Boysson-Bardies, B., & Vihman, M. M. (1991). Adaptation to language: Evidence from babbling and first words in four languages. Language, 67(2), 297–319. https://doi.org/10.1353/lan.1991.0045
   Web of Science ®Google Scholar
• Dollaghan, C., & Campbell, T. F. (1998). Nonword repetition and child language impairment. Journal of Speech, Language, & Hearing Research, 41(5), 1136–1146. https://doi.org/10.1044/jslhr.4105.1136
   PubMed Web of Science ®Google Scholar
• Donnay, G. F., Rankin, S. K., Lopez-Gonzalez, M., Jiradejvong, P., Limb, C. J., & Chialvo, D. R. (2014). Neural substrates of interactive musical improvisation: An fmri study of ‘trading fours’ in jazz. PLoS One, 9(2), e88665. https://doi.org/10.1371/journal.pone.0088665
   PubMed Web of Science ®Google Scholar
• Eadie, P., Conway, L., Hallenstein, B., Mensah, F., McKean, C., & Reilly, S. (2018). Quality of life in children with developmental language disorder. International Journal of Language & Communication Disorders, 53(4), 799–810. https://doi.org/10.1111/1460-6984.12385
   PubMed Web of Science ®Google Scholar
• Ebert, K. D., & Kohnert, K. (2016). Language learning impairment in sequential bilingual children. Language Teaching, 49(3), 301–338. https://doi.org/10.1017/S0261444816000070
   Web of Science ®Google Scholar
• Evans, J. L., & Brown, T. T. (2016). Chapter 72 - specific language impairment. In G. Hickok & S. L. SmallEds., Neurobiology of language (pp. 899–912). Academic Press. https://doi.org/10.1016/B978-0-12-407794-2.00072-9
   Google Scholar
• Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., André, E., Busso, C., & others. (2015). The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing. IEEE Transactions on Affective Computing, 7(2), 190–202. https://doi.org/10.1109/TAFFC.2015.2457417
   Web of Science ®Google Scholar
• Eyben, F., Wöllmer, M., & Schuller, B. (2010). Opensmile: The munich versatile and fast open-source audio feature extractor. In Proceedings of the 18th acm international conference on multimedia, October 25 - 29, 2010, Firenze Italy (pp. 1459–1462).
   Google Scholar
• Fiveash, A., Bedoin, N., Gordon, R. L., & Tillmann, B. (2021). Processing rhythm in speech and music: Shared mechanisms and implications for developmental speech and language disorders. Neuropsychology, 35(8), 771. https://doi.org/10.1037/neu0000766
   PubMed Web of Science ®Google Scholar
• García, A. M., Arias-Vergara, T. C., Vasquez-Correa, J., Nöth, E., Schuster, M., Welch, A. E., Bocanegra, Y., Baena, A., & Orozco-Arroyave, J. R. (2021). Cognitive determinants of dysarthria in parkinson’s disease: An automated machine learning approach. Movement Disorders, 36(12), 2862–2873. https://doi.org/10.1002/mds.28751
   PubMed Web of Science ®Google Scholar
• Gaspers, J., Thiele, K., Cimiano, P., Foltz, A., Stenneken, P., & Tscherepanow, M. (2012). An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques. In Proceedings of the 2nd acm sighit international health informatics symposium, January 28 - 30, 2012, Miami Florida USA (pp. 209–218).
   Google Scholar
• Gelsomini, M., Spitale, M., Beccaluva, E., Viola, L., & Garzotto, F. (2019). Reflex: Adaptive learning beyond the screen. In Proceedings of the 24th international conference on intelligent user interfaces: companion, March 16 - 20, 2019, Marina del Ray California (pp. 29–30).
   Google Scholar
• Gilbert, C., Ito, M., Kapadia, M., & Westheimer, G. (2000). Interactions between attention, context and learning in primary visual cortex. Vision Research, 40(10–12), 1217–1226. https://doi.org/10.1016/S0042-6989(99)00234-5
   PubMed Web of Science ®Google Scholar
• Gordon, R. L., Jacobs, M. S., Schuele, C. M., & McAuley, J. D. (2015). Perspectives on the rhythm-grammar link and its implications for typical and atypical language development. Annals of the New York Academy of Sciences, 1337(1), 16–25. https://doi.org/10.1111/nyas.12683
   PubMedGoogle Scholar
• Groskreutz, N. C., Groskreutz, M. P., & Higbee, T. S. (2011). Effects of varied levels of treatment integrity on appropriate toy manipulation in children with autism. Research in Autism Spectrum Disorders, 5(4), 1358–1369. https://doi.org/10.1016/j.rasd.2011.01.018
   Web of Science ®Google Scholar
• Hamidi, F. (2012). Digital tangible games for speech intervention. Technical Report CSE-2012-02. Department of Computer Science and Engineering.
   Google Scholar
• Hateli, B. (2022). The effect of non-directive play therapy on reduction of anxiety disorders in young children. Counselling and Psychotherapy Research, 22(1), 140–146. https://doi.org/10.1002/capr.12420
   Web of Science ®Google Scholar
• Heard, M., & Lee, Y. S. (2020). Shared neural resources of rhythm and syntax: An ale meta-analysis. Neuropsychologia, 137, 107284. https://doi.org/10.1016/j.neuropsychologia.2019.107284
   PubMed Web of Science ®Google Scholar
• Hegde, S., Shetty, S., Rai, S., & Dodderi, T. (2019). A survey on machine learning approaches for automatic detection of voice disorders. Journal of Voice, 33(6), 947–e11. https://doi.org/10.1016/j.jvoice.2018.07.014
   Web of Science ®Google Scholar
• Hengeveld, B., Voort, R., Hummels, C., Moor, J. D., van Balkom, H., Overbeeke, K., & van der Helm, A. (2008). The development of linguabytes: An interactive tangible play and learning system to stimulate the language development of toddlers with multiple disabilities. Advances in Human-Computer Interaction, 2008, 1–13. https://doi.org/10.1155/2008/381086
   Google Scholar
• Herdener, M., Humbel, T., Esposito, F., Habermeyer, B., Cattapan-Ludewig, K., & Seifritz, E. (2014). Jazz drummers recruit language-specific areas for the processing of rhythmic structure. Cerebral Cortex, 24(3), 836–843. https://doi.org/10.1093/cercor/bhs367
   PubMed Web of Science ®Google Scholar
• Hoff, E. (2013). Language development. Cengage Learning.
   Google Scholar
• Hutauruk, B. S. (2015). Children first language acquisition at age 1-3 years old in balata. IOSR Journal of Humanities & Social Science (IOSR-JHSS), 20(8), 51–57.
   Google Scholar
• Iceta, A., & Yoldi, M. (2002). Psychomotor development of the child and its evaluation in primary care. Anales del sistema sanitario de navarra, 25(2), 35–43. https://doi.org/10.23938/ASSN.0829
   PubMedGoogle Scholar
• Ishii, H. (2008). The tangible user interface and its evolution. Communications of the ACM, 51(6), 32–36. https://doi.org/10.1145/1349026.1349034
   Web of Science ®Google Scholar
• Jadan-Guerrero, J., Jaen, J., Carpio, M. A., & Guerrero, L. A. (2015). Kiteracy: A kit of tangible objects to strengthen literacy skills in children with down syndrome. In Proceedings of the 14th international conference on interaction design and children, June 21 - 24, 2015, Boston Massachusetts (pp. 315–318).
   Google Scholar
• Jensen, C. N., Burleson, W., & Sadauskas, J. (2012). Fostering early literacy skills in children’s libraries: Opportunities for embodied cognition and tangible technologies. In Proceedings of the 11th international conference on interaction design and children, Bremen Germany (pp. 50–59).
   Google Scholar
• Justice, L. M., Ahn, W.-Y., & Logan, J. A. (2019). Identifying children with clinical language disorder: An application of machine-learning classification. Journal of Learning Disabilities, 52(5), 351–365. https://doi.org/10.1177/0022219419845070
   PubMed Web of Science ®Google Scholar
• Kamide, Y. (2008). Anticipatory processes in sentence processing. Language and Linguistics Compass, 2(4), 647–670. https://doi.org/10.1111/j.1749-818X.2008.00072.x
   Google Scholar
• Karlsson, L. C., Soveri, A., Räsänen, P., Kärnä, A., Delatte, S., Lagerström, E., Mård, L., Steffansson, M., Lehtonen, M., & Laine, M. (2015). Bilingualism and performance on two widely used developmental neuropsychological test batteries. PLoS One, 10(4), e0125867. https://doi.org/10.1371/journal.pone.0125867
   PubMed Web of Science ®Google Scholar
• Kohlschein, C., Schmitt, M., Schüller, B., Jeschke, S., & Werner, C. J. (2017). A machine learning based system for the automatic evaluation of aphasia speech. In 2017 ieee 19th international conference on e-health networking, applications and services (healthcom), 12-15 Oct. 2017, Dalian, China (pp. 1–6).
   Google Scholar
• Kohnert, K. (2010). Bilingual children with primary language impairment: Issues, evidence and implications for clinical actions. Journal of Communication Disorders, 43(6), 456–473. https://doi.org/10.1016/j.jcomdis.2010.02.002
   PubMed Web of Science ®Google Scholar
• Kotz, S. A., Penhune, V., Henry, M., Large, E., Grahn, J., & Dalla Bella, S. (2014). Entrainment: A domain general cognitive timing mechanism? Procedia - Social & Behavioral Sciences, 126, 24–26. https://doi.org/10.1016/j.sbspro.2014.02.304
   Google Scholar
• Kristinsson, S., Zhang, W., Rorden, C., Newman-Norlund, R., Basilakos, A., Bonilha, L., Yourganov, G., Xiao, F., Hillis, A., & Fridriksson, J. (2021). Machine learning-based multimodal prediction of language outcomes in chronic aphasia. Human Brain Mapping, 42(6), 1682–1698. https://doi.org/10.1002/hbm.25321
   PubMed Web of Science ®Google Scholar
• Law, J., McKean, C., Murphy, C.-A., & Thordardottir, E., Law, J., McKean, C., Murphy, C.-A., Thordardottir, E. (2019). Managing children with developmental language disorder: Theory and practice across europe and beyond. Routledge. https://doi.org/10.4324/9780429455308
   Google Scholar
• Lefcourt, H. M., Lefcourt, H. M. (2014). Locus of control: Current trends in theory & research. Psychology Press. https://doi.org/10.4324/9781315798813
   Google Scholar
• Leonard, L. B. (2014). Children with specific language impairment. MIT press.
   Google Scholar
• Lövdén, M., Bäckman, L., Lindenberger, U., Schaefer, S., & Schmiedek, F. (2010). A theoretical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136(4), 659. https://doi.org/10.1037/a0020080
   PubMed Web of Science ®Google Scholar
• Lowder, M. W., & Gordon, P. C. (2015). Focus takes time: Structural effects on reading. Psychonomic Bulletin & Review, 22(6), 1733–1738. https://doi.org/10.3758/s13423-015-0843-2
   PubMed Web of Science ®Google Scholar
• Matias-Guiu, J. A., Diaz-Alvarez, J., Cuetos, F., Cabrera-Martín, M. N., Segovia-Rios, I., Pytel, V., Moreno-Ramos, T., Carreras, J. L., Matías-Guiu, J., & Ayala, J. L. (2019). Machine learning in the clinical and language characterisation of primary progressive aphasia variants. Cortex, 119, 312–323. https://doi.org/10.1016/j.cortex.2019.05.007
   PubMed Web of Science ®Google Scholar
• McLeod, B. D., Sutherland, K. S., Martinez, R. G., Conroy, M. A., Snyder, P. A., & Southam-Gerow, M. A. (2017). Identifying common practice elements to improve social, emotional, and behavioral outcomes of young children in early childhood classrooms. Prevention Science, 18(2), 204–213. https://doi.org/10.1007/s11121-016-0703-y
   PubMed Web of Science ®Google Scholar
• Mechling, L. C., & Savidge, E. J. (2011). Using a personal digital assistant to increase completion of novel tasks and independent transitioning by students with autism spectrum disorder. Journal of Autism and Developmental Disorders, 41(6), 687–704. https://doi.org/10.1007/s10803-010-1088-6
   PubMed Web of Science ®Google Scholar
• Mitchell, T., Buchanan, B., DeJong, G., Dietterich, T., Rosenbloom, P., & Waibel, A. (1990). Machine learning. Annual Review of Computer Science, 4(1), 417–433. https://doi.org/10.1146/annurev.cs.04.060190.002221
   Google Scholar
• Montessori, M. (2013). The montessori method (Vol. 1, pp. 375). Transaction publishers.
   Google Scholar
• Mulfari, D., Meoni, G., & Fanucci, L. (2018). Machine learning in assistive technology: A solution for people with dysarthria. In Proceedings of the 4th eai international conference on smart objects and technologies for social good, November 28-30, 2018, Bologna, Italy (pp. 308–309).
   Google Scholar
• Neils, J., & Aram, D. M. (1986). Family history of children with developmental language disorders. Perceptual and Motor Skills, 63(2), 655–658. https://doi.org/10.2466/pms.1986.63.2.655
   PubMed Web of Science ®Google Scholar
• Nilsson Benfatto, M., Öqvist Seimyr, G., Ygge, J., Pansell, T., Rydberg, A., Jacobson, C., & Lappe, M. (2016). Screening for dyslexia using eye tracking during reading. PloS One, 11(12), e0165508. https://doi.org/10.1371/journal.pone.0165508
   PubMed Web of Science ®Google Scholar
• Nonnis, A., & Bryan-Kinns, N. (2019). Mazi: Tangible technologies as a channel for collaborative play. In Proceedings of the 2019 chi conference on human factors in computing systems, May 4 - 9, 2019, Glasgow Scotland Uk (pp. 1–13).
   Google Scholar
• Norris, D., McQueen, J. M., & Cutler, A. (2003). Perceptual learning in speech. Cognitive Psychology, 47(2), 204–238. https://doi.org/10.1016/S0010-0285(03)00006-9
   PubMed Web of Science ®Google Scholar
• Nunn, G. D., & Nunn, S. J. (1993). Locus of control and school performance: Some implications for teachers. Education, 113(4), 636–641.
   Web of Science ®Google Scholar
• O’Brien, H. L., Cairns, P., & Hall, M. (2018). A practical approach to measuring user engagement with the refined user engagement scale (ues) and new ues short form. International Journal of Human-Computer Studies, 112, 28–39. https://doi.org/10.1016/j.ijhcs.2018.01.004
   Web of Science ®Google Scholar
• O’Brien, H. L., & Toms, E. G. (2010). The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology, 61(1), 50–69. https://doi.org/10.1002/asi.21229
   Web of Science ®Google Scholar
• Oller, D. K., Niyogi, P., Gray, S., Richards, J. A., Gilkerson, J., Xu, D., Yapanel, U., & Warren, S. F. (2010). Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development. Proceedings of the National Academy of Sciences, 107(30), 13354–13359. https://doi.org/10.1073/pnas.1003882107
   PubMed Web of Science ®Google Scholar
• Oller, D. K., Wieman, L. A., Doyle, W. J., & Ross, C. (1976). Infant babbling and speech. Journal of Child Language, 3(1), 1–11. https://doi.org/10.1017/S0305000900001276
   Web of Science ®Google Scholar
• Organization, W. H. (2004). International statistical classification of diseases and related health problems: Alphabetical index (Vol. 3). Direzione Centrale Salute Integrazione Sociosanitaria, Politiche Sociali e Famiglia, ItalyDirezione Centrale Salute Integrazione Sociosanitaria, Politiche Sociali e Famiglia, Italy.
   Google Scholar
• Owens, R. E., Jr. (2009). Language disorders. Pearson India.
   Google Scholar
• Pagliarini, E., Scocchia, L., Granocchio, E., Sarti, D., Stucchi, N., & Guasti, M. T. (2020). Timing anticipation in adults and children with developmental dyslexia: Evidence of an inefficient mechanism. Scientific Reports, 10(1), 1–15. https://doi.org/10.1038/s41598-020-73435-z
   PubMed Web of Science ®Google Scholar
• Paplikar, A., Alladi, S., Varghese, F., Mekala, S., Arshad, F., Sharma, M., & others. (2021). Bilingualism and its implications for neuropsychological evaluation. Archives of Clinical Neuropsychology, 36(8), 1511–1522. https://doi.org/10.1093/arclin/acab012
   Web of Science ®Google Scholar
• Patel, A. D. (1998). Syntactic processing in language and music: Different cognitive operations, similar neural resources? Music Perception, 16(1), 27–42. https://doi.org/10.2307/40285775
   Web of Science ®Google Scholar
• Perry, L. K., Kucker, S. C., Horst, J. S., & Samuelson, L. K. (2022). Late bloomer or language disorder? differences in toddler vocabulary composition associated with long-term language outcomes. Developmental Science, 26(4), e13342. https://doi.org/10.1111/desc.13342
   PubMed Web of Science ®Google Scholar
• Persici, V., Stucchi, N., & Arosio, F. (2019a). Predicting the future in rhythm and language: The anticipation abilities of a group of Italian-speaking children. In International conference 13 generative approaches to language acquisition (gala 13), sep, 2017, Palma De Mallorca, Spain.
   Google Scholar
• Persici, V., Stucchi, N., & Arosio, F. (2019b). Rhythmic and morphosyntactic predictions: The anticipation abilities of Italian children with developmental dyslexia.
   Google Scholar
• Pham, G., & Ebert, K. D. (2020). Diagnostic accuracy of sentence repetition and nonword repetition for developmental language disorder in vietnamese. Journal of Speech, Language, & Hearing Research, 63(5), 1521–1536. https://doi.org/10.1044/2020_JSLHR-19-00366
   PubMed Web of Science ®Google Scholar
• Rafi, A. (2017). Using machine learning to match assistive technology to people with disabilities. In P. Peter Cudd, & L. De Witte (Ed.), Harnessing the power of technology to improve lives (pp. 409–412). IOS Press.
   Google Scholar
• Rapin, I. (1996). Practitioner review: Developmental language disorders: A clinical update. Journal of Child Psychology and Psychiatry, 37(6), 643–655. https://doi.org/10.1111/j.1469-7610.1996.tb01456.x
   PubMed Web of Science ®Google Scholar
• Rapin, I., & Allen, D. (1983). Developmental language disorders: Nosologic considerations. In Kirk, Ursula (Ed.), Neuropsychology of Language, Reading, and Spelling (pp.155–184). Elsevier. https://doi.org/10.1016/B978-0-12-409680-6.X5001-X
   Google Scholar
• Rauschenberger, M., Baeza-Yates, R., & Rello, L. (2020). Screening risk of dyslexia through a web-game using language-independent content and machine learning. In Proceedings of the 17th international web for all conference (pp. 1–12).
   Google Scholar
• Raven, J. C., & Court, J. H. (1998). Raven’s progressive matrices and vocabulary scales (Vol. 759). Oxford pyschologists Press Oxford.
   Google Scholar
• Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372. https://doi.org/10.1037/0033-2909.124.3.372
   PubMed Web of Science ®Google Scholar
• Redmond, S. M. (2002). The use of rating scales with children who have language impairments. American Journal of Speech-Language Pathology, 11(2), 124–138. https://doi.org/10.1044/1058-0360(2002/013)
   Web of Science ®Google Scholar
• Rello, L., & Ballesteros, M. (2015). Detecting readers with dyslexia using machine learning with eye tracking measures. In Proceedings of the 12th international web for all conference, April 20-21, 2020, Taipei, Taiwan (pp. 1–8).
   Google Scholar
• Rescorla, L., & Mirak, J. (1997). Normal language acquisition. Seminars in Pediatric Neurology, 4(2), 70–76. https://doi.org/10.1016/S1071-9091(97)80022-8
   PubMedGoogle Scholar
• Rice, M. L., Hadley, P. A., & Alexander, A. L. (1993). Social biases toward children with speech and language impairments: A correlative causal model of language limitations. Applied Psycholinguistics, 14(4), 445–471. https://doi.org/10.1017/S0142716400010699
   Web of Science ®Google Scholar
• Robles-Bykbaev, V., Velásquez-Angamarca, V., Mosquera-Cordero, K., Calle-López, D., Robles-Bykbaev, Y., Pinos-Vélez, E., & León-Pesántez, A. (2018). A proposal of a virtual robotic assistant and a rule-based expert system to carry out therapeutic exercises with children with dyslalia. In 2018 ieee third ecuador technical chapters meeting (etcm), 15-19 Oct. 2018, Cuenca, Ecuador (pp. 1–6).
   Google Scholar
• Rogers, H., & Saklofske, D. (1985). Self-concepts, locus of control and performance expectations of learning disabled children. Journal of Learning Disabilities, 18(5), 273–278. https://doi.org/10.1177/002221948501800505
   PubMed Web of Science ®Google Scholar
• Sakin, B., & Bal, E. (2022). Language disorders and artificial intelligence. In Selamoglu, Zeliha (Ed.), Theory and Research in Health Sciences (Vol. 47083, pp.41–58). Serüven Publishing.
   Google Scholar
• Sansavini, A., Favilla, M. E., Guasti, M. T., Marini, A., Millepiedi, S., DiMartino, M. V., & others. (2021). Developmental language disorder: Early predictors, age for the diagnosis, and diagnostic tools. a scoping review. Brain Sciences, 11(5), 654. https://doi.org/10.3390/brainsci11050654
   PubMed Web of Science ®Google Scholar
• Schroeder, R. W., Twumasi-Ankrah, P., Baade, L. E., & Marshall, P. S. (2012). Reliable digit span: A systematic review and cross-validation study. Assessment, 19(1), 21–30. https://doi.org/10.1177/1073191111428764
   PubMed Web of Science ®Google Scholar
• Schwob, S., Eddé, L., Jacquin, L., Leboulanger, M., Picard, M., Oliveira, P. R., & Skoruppa, K. (2021). Using nonword repetition to identify developmental language disorder in monolingual and bilingual children: A systematic review and meta-analysis. Journal of Speech, Language, & Hearing Research, 64(9), 3578–3593. https://doi.org/10.1044/2021_JSLHR-20-00552
   PubMed Web of Science ®Google Scholar
• Shahin, M., Ahmed, B., Smith, D. V., Duenser, A., & Epps, J. (2019). Automatic screening of children with speech sound disorders using paralinguistic features. In 2019 ieee 29th international workshop on machine learning for signal processing (mlsp) (p. 1–5). https://doi.org/10.1109/MLSP.2019.8918725
   Google Scholar
• Shahin, M., Zafar, U., & Ahmed, B. (2020). The automatic detection of speech disorders in children: Challenges, opportunities, and preliminary results. IEEE Journal of Selected Topics in Signal Processing, 14(2), 400–412. https://doi.org/10.1109/JSTSP.2019.2959393
   Web of Science ®Google Scholar
• Sitdhisanguan, K., Chotikakamthorn, N., Dechaboon, A., & Out, P. (2012). Using tangible user interfaces in computer-based training systems for low-functioning autistic children. Personal and Ubiquitous Computing, 16(2), 143–155. https://doi.org/10.1007/s00779-011-0382-4
   Web of Science ®Google Scholar
• Slobin, D. I., & Bever, T. G. (1982). Children use canonical sentence schemas: A crosslinguistic study of word order and inflections. Cognition, 12(3), 229–265. https://doi.org/10.1016/0010-0277(82)90033-6
   PubMed Web of Science ®Google Scholar
• Smith, M. C. (2019). Sensory integration: Theory and practice. FA Davis.
   Google Scholar
• Snowling, M. J. (2013). Early identification and interventions for dyslexia: A contemporary view. Journal of Research in Special Educational Needs, 13(1), 7–14. https://doi.org/10.1111/j.1471-3802.2012.01262.x
   PubMedGoogle Scholar
• Speights Atkins, M., Bailey, D. J., & Boyce, S. E. (2020). Speech exemplar and evaluation database (seed) for clinical training in articulatory phonetics and speech science. Clinical Linguistics & Phonetics, 34(9), 878–886. https://doi.org/10.1080/02699206.2020.1743761
   PubMed Web of Science ®Google Scholar
• Spencer, S. (2013). Language disorders from infancy through adolescence: Listening, speaking, reading, writing and communicating 4th edn edited by rheapaul and courtenay f. norbury (st. louis, mo: Elsevier mosby, 2012)[pp. 756]. isbn 978-0-323-07184-0.£ 75.99
   Google Scholar
• Spitale, M., Gelsomini, M., Beccaluva, E., Viola, L., & Garzotto, F. (2019). Meeting the needs of people with neuro-developmental disorder through a phygital approach. In Proceedings of the 13th biannual conference of the italian sigchi chapter: Designing the next interaction, September 23-25, 2019, Padua, Italy (pp. 1–10).
   Google Scholar
• Starr, S. (1975). The relationship of single words to two-word sentences. Child Development, 46(3), 701–708. https://doi.org/10.2307/1128568
   PubMed Web of Science ®Google Scholar
• Suthar, K., Yousefi Zowj, F., Speights Atkins, M., He, Q. P., & Lu, H. H. S. (2022). Feature engineering and machine learning for computer-assisted screening of children with speech disorders. PLoS Digital Health, 1(5), e0000041. https://doi.org/10.1371/journal.pdig.0000041
   PubMedGoogle Scholar
• Tager-Flusberg, H. (1999). Language development in atypical children. In Barret, Martin (Ed.), The Development of Language (Vol. 1, pp. 311–348). Plural PublishingPsychology Press.
   Google Scholar
• Tessmer, M., & Richey, R. C. (1997). The role of context in learning and instructional design. Educational Technology Research & Development, 45(2), 85–115. https://doi.org/10.1007/BF02299526
   Web of Science ®Google Scholar
• Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-sne. Journal of Machine Learning Research, 9(11), 2579–2605. http://jmlr.org/papers/v9/vandermaaten08a.htm
   Google Scholar
• Vassar, R., Schadl, K., Cahill-Rowley, K., Yeom, K., Stevenson, D., & Rose, J. (2020). Neonatal brain microstructure and machine-learning-based prediction of early language development in children born very preterm. Pediatric Neurology, 108, 86–92. https://doi.org/10.1016/j.pediatrneurol.2020.02.007
   PubMed Web of Science ®Google Scholar
• Visser-Bochane, M. I., Gerrits, E., van der Schans, C. P., Reijneveld, S. A., & Luinge, M. R. (2017). Atypical speech and language development: A consensus study on clinical signs in the netherlands. International Journal of Language & Communication Disorders, 52(1), 10–20. https://doi.org/10.1111/1460-6984.12251
   PubMed Web of Science ®Google Scholar
• Vona, F., Torelli, E., Beccaluva, E., & Garzotto, F. (2020). Exploring the potential of speech-based virtual assistants in mixed reality applications for people with cognitive disabilities. In Proceedings of the international conference on advanced visual interfaces 28 September 2020- 2 October 2020 Salerno Italy (pp. 1–9).
   Google Scholar
• Walters, C. E., Jr., Nitin, R., Margulis, K., Boorom, O., Gustavson, D. E., Bush, C. T., & others. (2020). Automated phenotyping tool for identifying developmental language disorder cases in health systems data (apt-dld): A new research algorithm for deployment in large-scale electronic health record systems. Journal of Speech, Language, & Hearing Research, 63(9), 3019–3035. https://doi.org/10.1044/2020_JSLHR-19-00397
   PubMed Web of Science ®Google Scholar
• Wang, C.-Y., & Lai, A.-F. (2011). Development of a mobile rhythm learning system based on digital game-based learning companion. In Edutainment technologies. educational games and virtual reality/augmented reality applications: 6th international conference on e-learning and games, edutainment 2011. Taipei, Taiwan, (pp. 92–100).
   Google Scholar
• Weinert, S. (1992). Deficits in acquiring language structure: The importance of using prosodic cues. Applied Cognitive Psychology, 6(6), 545–571. https://doi.org/10.1002/acp.2350060607
   Web of Science ®Google Scholar
• Wells, G. (1986). The meaning makers: Children learning language and using language to learn. ERIC.
   Google Scholar
• Willis, J. (2008). Teaching the brain to read: Strategies for improving fluency, vocabulary, and comprehension. ASCD.
   Google Scholar
• Wittke, K., & Spaulding, T. J. (2018). Which preschool children with specific language impairment receive language intervention? Language, Speech, and Hearing Services in Schools, 49(1), 59–71. https://doi.org/10.1044/2017_LSHSS-17-0024
   PubMed Web of Science ®Google Scholar
• Xu, D. (2005). Tangible user interface for children-an overview. In Proc. of the uclan department of computing conference. Preston PR1 2HE, UK
   Google Scholar
• Xue, W., Cucchiarini, C., van Hout, R., & Strik, H. (2019). Acoustic correlates of speech intelligibility. the usability of the egemaps feature set for atypical speech.
   Google Scholar
• Zare, M., Rezvani, Z., & Benasich, A. A. (2016). Automatic classification of 6-month-old infants at familial risk for language-based learning disorder using a support vector machine. Clinical Neurophysiology, 127(7), 2695–2703. https://doi.org/10.1016/j.clinph.2016.03.025
   PubMed Web of Science ®Google Scholar
• Zentall, S. S., & Kruczek, T. (1988). The attraction of color for active attention-problem children. Exceptional Children, 54(4), 357–362. https://doi.org/10.1177/001440298805400411
   PubMed Web of Science ®Google Scholar
• Zhang, X., Qin, F., Chen, Z., Gao, L., Qiu, G., & Lu, S. (2020). Fast screening for children’s developmental language disorders via comprehensive speech ability evaluation—using a novel deep learning framework. Annals of Translational Medicine, 8(11), 707–707. https://doi.org/10.21037/atm-19-3097
   PubMed Web of Science ®Google Scholar
• Zhou, Z.-H. (2021). Machine learning. Springer Nature. https://doi.org/10.1007/978-981-15-1967-3
   Google Scholar