Published October 1, 2025 | Version v1
Journal article Open

Frequent Articulation Disorders in Children

Description

Millions of children worldwide suffer from articulation disorders. An essential part of their treatment requires performing home exercises prescribed by their Speech Language Pathologist.Hence, academic institutions and companies are developing algorithms to address the correct classification of good versus poor phoneme articulation. As of today, the efforts to cover all the phonemes in the English language provide less than 90% accuracy. TIK TALK to Me Ltd., an Israeli company that develops methods and devices for treating speech disorders, conducted a large-scale study on children's most frequent articulation disorders. Over more than 24 months, the company accumulated records from some 250 children, ages 3 to 12, treated by 45 Speech Language Pathologists (SLPs) in the US. The metadata analysis obtained from the above records shows that 80% of the children required treatment on one or more of just 6 out of the 44 phonemes in the English language. The significance of the above findings is not just of academic interest to the community of speech-language pathologists. Companies and researchers should prioritize reaching top performance in the six most frequent articulation problems.

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Dates

Copyrighted
2022

References

  • [1] Long Zhang, at all, "End-to-End Automatic Pronunciation Error Detection Based on Improved Hybrid CTC/Attention Architecture, " Sensors 2020, https://doi.org/10.3390/s20071809 [2] Mostafa Shahin and Beena Ahmed, "Anomaly detection based pronunciation verification approach using speech attribute features,"Speech Communication, 111 (2019) 29–43 [3] Franco H., Neumeyer L., Digalakis V., and Ronen O., "Combination of Machine Scores for Automatic Grading of Pronunciation Quality,"Speech Communication, vol. 30, no. 2, pp. 121-130, 2000. [4] Ito A., Lim Y., Suzuki M., and Makino S., "Pronunciation Error Detection Method Based on Error Rule Clustering Using A Decision Tree, " in Proceedings of 9th European Conference on Speech Communication and Technology, Lisbon, pp. 173-176, 2005. [5] Strik H., Truong K., De-Wet F., and Cucchiarinia C., "Comparing Different Approaches for Automatic Pronunciation Error Detection, " Speech Communication, vol. 51, no. 10, pp. 845-852, 2009. [6] Wei S., Hu G., Hu Y., and Wang R., "A New Method for Mispronunciation Detection Using Support Vector Machine Based on Pronunciation Space Models," Speech Communication, vol. 51, no. 10, pp. 896-905, 2009. [7] Zahid S., Hussain F., Rashid M., Yousaf M., and Habib H., "Optimized Audio Classification And Segmentation Algorithm by Using Ensemble Methods," Mathematical Problems in Engineering, vol. 2015, pp. 1-11, 2015. [8] Vladimir Tregubov, "Using Voice Recognition in E-learning System to reduce Educational inequality During COVID -19, International Journal of Computer Science Engineering and Applications (IJCSEA)Vol. 11, No 2/3/4, August 2021.