Approximate State-Transition Modeling of Language Disorder Portrayals in Media

Authors

  • Suleiman Ibrahim Mohammad

    Electronic Marketing and Social Media, Economic and Administrative Sciences, Zarqa University, Zarqa P.O. Box 132010, Jordan

    Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia
  • Yogeesh Nijalingappa

    Department of Mathematics, Government First Grade College, Tumkur 572101, India
  • Natarajan Raja

    Department of Visual Communication, Sathyabama Institute of Science and Technology, Chennai 600001, India
  • Hanan Jadallah

    Electronic Marketing and Social Media, Economic and Administrative Sciences, Zarqa University, Zarqa P.O. Box 132010, Jordan
  • Yunus Jumaniyozov

    Department of Psychology and Pedagogy, Urgench State University, Urgench 220100, Uzbekistan
  • Asokan Vasudevan

    Faculty of Business and Communications, INTI International University, Nilai 71800, Malaysia

    Faculty of Management, Shinawatra University, Samkhok 12160, Thailand

    Business Administration and Management, Wekerle Business School, 1083 Budapest, Hungary
  • Xulkar Kasimova

    Department of Psychology and Medicine, Mamun University, Khiva 220900, Uzbekistan

DOI:

https://doi.org/10.30564/fls.v7i11.11493
Received: 5 August 2025 | Revised: 1 September 2025 | Accepted: 8 September 2025 | Published Online: 28 October 2025

Abstract

Quantitative analysis of how cognitive-linguistic impairments are depicted in broadcast and digital outlets requires dynamic models that capture both disfluency patterns and sampling noise. This paper presents a first-order Markov-chain framework enhanced by a controlled smoothing operator to mitigate spurious low-frequency transitions. We define an empirical transition matrix from annotated utterance segments (fluent, hesitation, error) and apply a Gaussian-kernel–based approximation to produce a smoothed stochastic matrix with provable perturbation bounds. Theoretical results guarantee that the t-step transition error grows at most linearly with the smoothing magnitude and that stationary-distribution shifts remain bounded by the ratio of approximation error to the spectral gap. On a large, multi-source corpus (≈20,000 segments), cross-validated smoothing achieves a 12% perplexity reduction over the unsmoothed chain and reduces cumulative error-state estimation deviation by 19% compared to a threshold-based baseline. A compact case study further illustrates the bias–variance trade-off inherent in smoothing: aggressive approximation on small sequences can dramatically inflate unlikely transitions, underscoring the need for corpus-sensitive parameter tuning. These findings demonstrate that kernel-smoothed stochastic models offer interpretable, computationally efficient tools for analyzing disfluency dynamics over time. Future work will explore higher-order dependencies, nonstationary transition matrices, and hybrid deep‐learning integrations to capture richer contextual patterns in discourse sequences.

Keywords:

Markov Chain Smoothing; Disfluency Dynamics; Kernel Approximation; Spectral-Gap Analysis; Cross-Validation; Sequence Prediction

References

[1] Hammar, Å., Ronold, E.H., Rekkedal, G.Å., 2022. Cognitive Impairment and Neurocognitive Profiles in Major Depression—A Clinical Perspective. Frontiers in Psychiatry. 13, 764374. DOI: https://doi.org/10.3389/fpsyt.2022.764374

[2] Mohammad, A.A.S., Alolayyan, M.N., Al- Daoud, K.I., et al., 2024. Association between Social Demographic Factors and Health Literacy in Jordan. Journal of Ecohumanism. 3(7). DOI: https://doi.org/10.62754/joe.v3i7.4384

[3] Jurafsky, D., 2003. Probabilistic Modeling in Psycholinguistics: Linguistic Comprehension and Production. In: Bod, R., Hay, J., Jannedy, S. (eds.). Probabilistic Linguistics. The MIT Press: Cambridge, MA, USA. pp. 39–96. DOI: https://doi.org/10.7551/mitpress/5582.003.0006

[4] Al-Rahmi, W.M., Al-Adwan, A.S., Al-Maatouk, Q., et al., 2023. Integrating Communication and Task–Technology Fit Theories: The Adoption of Digital Media in Learning. Sustainability. 15(10), 8144. DOI: https://doi.org/10.3390/su15108144

[5] Zhang, Y., Patel, S., 2019. Smoothing noisy transition matrices. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 2019; pp. 234–242.

[6] Mohammad, A.A.S., Shelash, S.I., Taher Saber, I., et al., 2025. Internal Audit Governance Factors and their effect on the Risk-Based Auditing Adoption of Commercial Banks in Jordan. Data and Metadata. 4, 464. DOI: https://doi.org/10.56294/dm2025464

[7] Quintero, A., Verbeke, G., Bruyneel, L., et al., 2021. Bayesian analysis of differential effects in multi-group regression methods. Statistical Modelling. 21(3), 244–263. DOI: https://doi.org/10.1177/1471082X19881844

[8] Hujran, O., Al-Debei, M.M., Al-Adwan, A.S., et al., 2023. Examining the antecedents and outcomes of smart government usage: An integrated model. Government Information Quarterly. 40(1), 101783. DOI: https://doi.org/10.1016/j.giq.2022.101783

[9] Campi, M., Peters, G.W., Toczydlowska, D., 2023. Ataxic speech disorders and Parkinson's disease diagnostics via stochastic embedding of empirical mode decomposition. PLOS ONE. 18(4), e0284667. DOI: https://doi.org/10.1371/journal.pone.0284667

[10] Mohammad, A.A.S., 2025. The impact of COVID-19 on digital marketing and marketing philosophy: evidence from Jordan. International Journal of Business Information Systems. 48(2), 267–281. DOI: https://doi.org/10.1504/IJBIS.2025.144382

[11] Al Daboub, R.S., Al-Madadha, A., Al-Adwan, A.S., 2024. Fostering firm innovativeness: Understanding the sequential relationships between human resource practices, psychological empowerment, innovative work behavior, and firm innovative capability. International Journal of Innovation Studies. 8(1), 76–91. DOI: https://doi.org/10.1016/j.ijis.2023.12.001

[12] Weissbart, H., Martin, A.E., 2024. The structure and statistics of language jointly shape cross-frequency neural dynamics during spoken language comprehension. Nature Communications. 15(1), 8850. DOI: https://doi.org/10.1038/s41467-024-53128-1

[13] Mohammad, A.A.S., Suleiman Ibrahim Shelash Mohammad, Al Daoud, K.I., et al., 2025. Optimizing the Value Chain for Perishable Agricultural Commodities: A Strategic Approach for Jordan. Research on World Agricultural Economy. 465–478. DOI: https://doi.org/10.36956/rwae.v6i1.1571

[14] Mohammad, A.A.S., Mohammad, S.I.S., Oraini, B.A., et al., 2025. Data security in digital accounting: A logistic regression analysis of risk factors. International Journal of Innovative Research and Scientific Studies. 8(1), 2699–2709. DOI: https://doi.org/10.53894/ijirss.v8i1.5044

[15] Yogeesh, N., 2024. Fuzzy Graph Dominance for Networked Communication Optimization. In: Sharma, V., Balusamy, B., Ferrari, G. (eds.). Wireless Communication Technologies. CRC Press, Boca Raton, FL, USA. pp. 36–65. DOI: https://doi.org/10.1201/9781003389231-3

[16] Mohammad, A.A.S., Mohammad, S.I.S., Al-Daoud, K.I., et al., 2025. Digital ledger technology: A factor analysis of financial data management practices in the age of blockchain in Jordan. International Journal of Innovative Research and Scientific Studies. 8(2), 2567–2577. DOI: https://doi.org/10.53894/ijirss.v8i2.5737

[17] Shlash Mohammad, A.A., Shelash Al-Hawary, S.I., Hindieh, A., et al., 2025. Intelligent Data-Driven Task Offloading Framework for Internet of Vehicles Using Edge Computing and Reinforcement Learning. Data and Metadata. 4, 521. DOI: https://doi.org/10.56294/dm2025521

[18] Wenzel, T., Iske, A., 2024. Spectral alignment of kernel matrices and applications. arXiv preprint arXiv:2409.04263. DOI: https://doi.org/10.48550/ARXIV.2409.04263

[19] Mohammad, A.A.S., Alshebel, M., Al Oraini, B., et al., 2024. Research on Multimodal College English Teaching Model Based on Genetic Algorithm. Data and Metadata. 3, 421. DOI: https://doi.org/10.56294/dm2024421

[20] Sun, L., Nikolaev, A.G., 2016. Mutual information based matching for causal inference with observational data. Journal of Machine Learning Research. 17(199), 1–31.

[21] Mohammad, A.A.S., Mohammad, S.I., Vasudevan, A., et al., 2025. On The Numerical Solution of Bagley-Torvik Equation Using The M¨ Untz-Legendre Wavelet Collocation Method. Computational Methods for Differential Equations. 13(3), 968–979.

[22] Mori, A., 2025. A Simplex Model of Long Pathways in the Brain Related to the Minimalist Program in Linguistics. Symmetry. 17(2), 207. DOI: https://doi.org/10.3390/sym17020207

[23] Liu, H., Cai, Y., Ou, Z., et al., 2023. Building Markovian Generative Architectures Over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems. In Proceedings of the 2022 IEEE Spoken Language Technology Workshop (SLT). Doha, Qatar, 9 January 2023; pp. 382–389. DOI: https://doi.org/10.1109/SLT54892.2023.10023191

[24] Schulz, J., Kvaløy, J.T., Engan, K., et al., 2020. State transition modeling of complex monitored health data. Journal of Applied Statistics. 47(11), 1915–1935. DOI: https://doi.org/10.1080/02664763.2019.1698523

[25] Jane, J B., Ganesh, E.N,. 2019. A review on big data with machine learning and fuzzy logic for better decision making. International Journal of Scientific & Technology Research. 8(10), 1221–1225.

[26] Yogeesh, N., 2025. Applying Fuzzy Data Science in Generative AI for Healthcare. In: Gayathri, N., Rakesh Kumar, S., Chandran, R., et al. (eds.). Generative AI. Wiley: Hoboken, NJ,USA. pp. 215–248. DOI: https://doi.org/10.1002/9781394302932.ch10

[27] Stewart, G.W., 1990. Matrix Perturbation Theory. Academic Press: San Diego, CA, USA. pp 30–41.

[28] Bickel, P., Doksum, K., 2007. Mathematical Statistics: Basic Ideas and Selected Topics, 2nd ed. Springer: New York, NY, USA. pp 125–139.

[29] Levin, D., Peres, Y., Wilmer, E., 2009. Markov Chains and Mixing Times. American Mathematical Society: Providence, RI, USA. pp 117–123.

[30] Davis, T.A., 2006. Direct Methods for Sparse Linear Systems. Society for Industrial and Applied Mathematics: Philadelphia, PA , USA. pp 47–51.

[31] Golub, G.H., Van Loan, C.F., 2013. Matrix Computations, 4th ed. Johns Hopkins University Press: Baltimore, MD, USA. pp 12–27.

[32] Yogeesh, N., 2024. Solving Fuzzy Nonlinear Optimization Problems Using Evolutionary Algorithms. In: Mukherjee, G., Basu Mallik, B., Kar, R., et al. (eds.). Advances on Mathematical Modeling and Optimization with Its Applications. CRC Press: New York, NY, USA. pp. 76–95. DOI: https://doi.org/10.1201/9781003387459-6

[33] Stewart, W.J., 2009. Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press: Princeton, NJ, USA. pp 193–228.

[34] Jurafsky, D., Martin, J.H., 2020. Speech and Language Processing, 3rd ed. Prentice Hall: Upper Saddle River, NJ, USA. pp 202–235.

[35] Yogeesh, N., Girija, D.K., Rashmi, M., et al., 2023. Exploring the potential of fuzzy domination graphs in aquatic animal health and survival studies. Journal of Survey in Fisheries Sciences. 10(4 Suppl), 3133–3147.

[36] Dietterich, T.G., 1998. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation. 10(7), 1895–1923. DOI: https://doi.org/10.1162/089976698300017197

[37] Schuster, M., Paliwal, K.K., 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing. 45(11), 2673–2681. DOI: https://doi.org/10.1109/78.650093

[38] Vaswani, A., Shazeer, N., Parmar, N., et al., 2017. Attention Is All You Need. arXiv preprint. arXiv: 1706.03762. DOI: https://doi.org/10.48550/ARXIV.1706.03762

[39] MacWhinney, B., 2000. The CHILDES Project: Tools for Analyzing Talk, 3rd ed. Lawrence Erlbaum: Mahwah, NJ, USA. pp 59–75.

[40] MacWhinney, B., Fromm, D., Forbes, M., et al., 2011. AphasiaBank: Methods for studying discourse. Aphasiology. 25(11), 1286–1307. DOI: https://doi.org/10.1080/02687038.2011.589893

[41] Rissanen, J., 1983. A universal data compression system. IEEE Transactions on Information Theory. 29(5), 656–664. DOI: https://doi.org/10.1109/TIT.1983.1056741

[42] Willems, F.M.J., Shtarkov, Y.M., Tjalkens, T.J., 1995. The context-tree weighting method: basic properties. IEEE Transactions on Information Theory. 41(3), 653–664. DOI: https://doi.org/10.1109/18.382012

[43] Nijalingappa, Y., Setty, G.D.K., Madan, R., et al., 2025. Directable zeros in fuzzy logics: A study of functional behaviours and applications. In Proceedings of the 6th INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING: IConIC2K23, Chennai, India, 28–29 April 2023; p. 020091. DOI: https://doi.org/10.1063/5.0254157

[44] Gelman, A., Carlin, J.B., Stern, H.S., et al., 2013. Bayesian Data Analysis, 3rd ed. Chapman and Hall/CRC: Boca Raton, FL, USA. pp 274–292.

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How to Cite

Mohammad, S. I., Nijalingappa, Y., Raja, N., Jadallah, H., Jumaniyozov, Y., Vasudevan, A., & Xulkar Kasimova. (2025). Approximate State-Transition Modeling of Language Disorder Portrayals in Media. Forum for Linguistic Studies, 7(11), 1508–1526. https://doi.org/10.30564/fls.v7i11.11493

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Vol. 7 , Iss. 11 (November 2025)

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Copyright © 2025 Suleiman Ibrahim Mohammad, Yogeesh Nijalingappa, Natarajan Raja, Hanan Jadallah, Yunus Jumaniyozov, Asokan Vasudevan, Xulkar Kasimova

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