Analysis of Linguistic Disfluencies in Bilingual Children’s Discourse
Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1317100Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 782
 Shapiro, David Allen, “A Collaborative Journey to Fluency Freedom, 2nd Edition, Pro ed, 2011.
 Conture, E. G.,” Stuttering: Its nature, diagnosis, and treatment” (3rd Ed.). Boston: Allyn and Bacon, 2005
 Adams, M., ”The demands and capacities model I: theoretical elaborations. Journal of Fluency Disorders, Vol. 15, pp. 135-141, 1990.
 Thordardottir, E. T. & Ellis 78Weismer, S.,” Content mazes and filled pauses in narrative language samples of children with specific language impairment” Brain and Cognition, 48 (2-3), 587-592, 2002.
 Miller, J. F., Long, S., McKinley, N., Thormann, S., Jones, M. A., & Nockerts, A., “Language Sample Analysis II: The Wisconsin guide”, Madison, WI: Wisconsin Department of Public Instruction, 2005.
 Dehak, N.et. al., “Modeling Prosodic Features with Joint Factor Analysis for Speaker Verification”, Audio, Speech and Language Processing, September 2007, Volume 15, pp.2095-21035.
 A. Veiga, S. Candeias, C. Lopes and F. Perdigao, “Characterization of Hesitation using Acoustic Models, “International Congress of Phonetic Sciences – ICPhs XVII, 2011.
 H. Moniz, I. Trancoso and A. I. Mata, “Classification of disfluent phenomena as fluent communicative devices in specific prosodic context,” in Interspeech 2009.
 H. Moniz, F. Batista, I. Trancosi and A. I. M. da Silva, “Prosodic context-based analysis of disfluencies,” in Interspeech 2012.
 Guo. L., Tomblin J. B., Samelson V., “Speech disruptions in the narratives of English-speaking children with specific language impairment, Journal of Speech, Language and Hearing Research, Vol.51(3), pp. 722-738.
 Akxutina T. V., ”Language Production: Neurolinguistic Syntactic Analaysis”, Moscow, 1989.
 Schegloff E. A, Jefferson, G.Sacks H., “Preference for self-correction in the organization of repair in conversation, Language, Vol.53(2), pp.361-381.
 Retrieved November 13, 2017, https://www.pinterest.com/Lcusick4288/kindergarten-writing-ideas/.
 Jamshid Lou, Paria & Johnson, Mark., “Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model”. 547-553. 10.18653/v1/P17-2087, 2017.
 Ferguson, James & Durrett, Greg & Klein, Dan., ”Disfluency Detection with a Semi-Markov Model and Prosodic Features”, 257-262. 10.3115/v1/N15-1029, 2015.
 L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition," Proceedings of the IEEE, vol. 77, pp. 257-286, 1989.