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Common Turkic Alphabet and the Impact of Phonetic Structure on Natural Language Processing Applications: An Exploratory Review and Future Directions

Year 2025, Volume: 18 Issue: 1, 44 - 56, 26.06.2025
https://doi.org/10.54525/bbmd.1622149

Abstract

Phonetic analysis is a branch of Natural Language Processing (DDİ) that examines how sounds are produced during speech and how words are related to these sounds. Because computers must understand both the mechanism of sound formation and the lexical, syntactic, and semantic aspects of languages, this is a highly demanding task. Although its historical roots date back centuries, in recent years researchers have adopted a broader perspective on phonetic analysis by focusing on various languages spoken worldwide. One of the greatest challenges in phonetics and phonology is achieving consistent results in tasks involving language recognition and processing (such as speech recognition and speech synthesis). In this study, the definitions of phonetics and phonology are addressed, and the methods employed by different researchers are examined. Additionally, the potential phonetic impacts of the Common Turkic Alphabet are discussed, providing detailed information about this alphabet. Furthermore, proposals for new algorithms or metrics to measure writing-pronunciation alignment and future directions in this field are highlighted.

References

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Ortak Türk Abecesi ve Ses Yapısının Doğal Dil İşleme Uygulamaları Üzerindeki Etkisi: Keşifsel bir Derleme ve Gelecek Yönelimler

Year 2025, Volume: 18 Issue: 1, 44 - 56, 26.06.2025
https://doi.org/10.54525/bbmd.1622149

Abstract

Fonetik analiz, konuşma sırasında seslerin nasıl üretildiğini ve sözcüklerin seslerle nasıl ilişkilendirildiğini inceleyen Doğal Dil İşleme (DDİ) konusunun bir dalıdır. Bilgisayarların bu süreci anlayıp değerlendirebilmesi, hem sesin oluşum mekanizmasını hem de dillerin sözcüksel, sözdizimsel ve anlamsal yönlerini dikkate almayı gerektirdiğinden oldukça zorlu bir çalışmadır. Tarihsel kökleri yüzyıllar öncesine uzansa da, son yıllarda araştırmacılar dünya çapında konuşulan çeşitli dilleri ele alarak fonetik analizi daha geniş bir bakış açısıyla değerlendirmiştir. Fonetik ve fonolojinin en büyük güçlüklerinden biri, dili tanıma ve işleme süreçlerinde (örneğin konuşma tanıma ve konuşma sentezi) tutarlı sonuçlar elde etmektir. Bu çalışmada, fonetik ve fonoloji kavramlarının tanımlarına değinilirken, farklı araştırmacıların benimsediği yöntemler incelenmektedir. Ek olarak, Ortak Türk Alfabesinin potansiyel fonetik etkileri tartışılarak bu alfabe hakkında detaylı bilgiler sunulmaktadır. Ayrıca, yazı-telaffuz uyumunu ölçmek için yeni algoritmaların veya metriklerin geliştirilmesine yönelik öneriler ve gelecekte izlenebilecek yönelimler de vurgulanmaktadır.

References

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  • Atabey, İ. Türkçe-Yazı İlişkisi ve Bağımsızlıklarının 30. Yılında Türk Cumhuriyetlerinde Abece, International Journal of Volga-Ural and Turkestan Studies, 6(2), 207-220, 2024.
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  • Aliyeva, E. Yeni Uygur Türkçesinin Dil Özellikleri ve Azerbaycan Türkçesiyle Ortaklıklar, Akademik Tarih ve Düşünce Dergisi, 9(2), 484-499, 2022.
  • Seddiq, Y., Alotaibi, Y. A., Selouani, S. A., Meftah, A. H. Distinctive phonetic features modeling and extraction using deep neural networks, IEEE Access, 7, 81382-81396, 2019.
  • Huang, Z., Epps, J., Joachim, D., Sethu, V. Natural language processing methods for acoustic and landmark event-based features in speech-based depression detection, IEEE Journal of Selected Topics in Signal Processing, 14(2), 435-448, 2019.
  • Kukanov, I., Trong, T. N., Hautamäki, V., Siniscalchi, S. M., Salerno, V. M., Lee, K. A. Maximal figure-of-merit framework to detect multi-label phonetic features for spoken language recognition, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 682-695, 2020.
  • Nair, J., Ahammed, R. English to Indian Language and Back Transliteration with Phonetic Transcription for Computational Linguistics Tools based on Conventional Transliteration Schemes, 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT), IEEE, 2021, pp. 1-6.
  • Vykhovanets, V. S., Du, J., Sakulin, S. A. An overview of phonetic encoding algorithms, Automation and Remote Control, 81, 1896-1910, 2020.
  • Kavros, A., Tzitzikas, Y. SoundexGR: An algorithm for phonetic matching for the Greek language, Natural Language Engineering, 29(5), 1305-1340, 2023.
  • El-Imam, Y. A., Don, Z. M. Rules and algorithms for phonetic transcription of standard Malay, IEICE Transactions on Information and Systems, 88(10), 2354-2372, 2005.
  • Almeida, G. A. D. M. Using phonetic knowledge in tools and resources for Natural Language Processing and Pronunciation Evaluation, Doktora Tezi, Universidade de São Paulo, 2016.
  • Żelasko, P., Moro-Velázquez, L., Hasegawa-Johnson, M., Scharenborg, O., Dehak, N. That sounds familiar: an analysis of phonetic representations transfer across languages, arXiv preprint arXiv:2005.08118, 2020.
  • Nazarov, A. I. Learning phonetically and phonologically natural classes through constraint indexation, Proceedings of the Annual Meetings on Phonology, 2021.
  • Fang, A., Filice, S., Limsopatham, N., Rokhlenko, O. Using phoneme representations to build predictive models robust to ASR errors, Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 2020, pp. 699-708.
  • Koffi, E. A tutorial on acoustic phonetic feature extraction for automatic speech recognition (ASR) and text-to-speech (TTS) applications in African languages, Linguistic Portfolios, 9(1), 11, 2020.
  • Lee, C. Y., Zhang, Y., Glass, J. Joint learning of phonetic units and word pronunciations for ASR, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013, pp. 182-192.
  • Żelasko, P., Feng, S., Velazquez, L. M., Abavisani, A., Bhati, S., Scharenborg, O., Dehak, N. Discovering phonetic inventories with crosslingual automatic speech recognition, Computer Speech & Language, 74, 101358, 2022.
  • Ungurean, C., Burileanu, D. An advanced DDİ framework for high-quality Text-to-Speech synthesis, 2011 6th Conference on Speech Technology and Human-Computer Dialogue (SpeD), IEEE, 2011, pp. 1-6.
  • Kurihara, K. Phonetic and Prosodic Features for Sequence-to-Sequence Acoustic Modeling on Japanese Text-to-Speech and Their Estimation, 2024.
  • Mukit, M., Rabbi, M. M. H., Ahmed, M. M., Latif, S., Saha, S. Sentiment Analysis on Bangla and Phonetic Bangla Reviews: A Product Rating Procedure using DDİ and Machine Learning, 2023 IEEE 9th International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, 2023, pp. 433-437.
  • Peng, H., Ma, Y., Poria, S., Li, Y., Cambria, E. Phonetic-enriched text representation for Chinese sentiment analysis with reinforcement learning, Information Fusion, 70, 88-99, 2021.
  • Kabir, M. M., Mridha, M. F., Shin, J., Jahan, I., Ohi, A. Q. A survey of speaker recognition: Fundamental theories, recognition methods and opportunities, IEEE Access, 9, 79236-79263, 2021.
  • Ohi, A. Q., Mridha, M. F., Hamid, M. A., Monowar, M. M. Deep speaker recognition: Process, progress, and challenges, IEEE Access, 9, 89619-89643, 2021.
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  • Hanumanthappa, M., Rashmi, S., Reddy, M. V. Metrics for evaluating phonetics machine translation in Natural Language Processing through modified Edit Distance algorithm-A naive approach, 2015 International Conference on Computer Communication and Informatics (ICCCI), IEEE, 2015, pp. 1-7.
  • Randhawa, E. S., Saroa, E. C. Study of spell checking techniques and available spell checkers in regional languages: a survey, International Journal For Technological Research In Engineering, 2(3), 148-151, 2014.
  • Tan, M., Chen, D., Li, Z., Wang, P. Spelling error correction with BERT based on character-phonetic, 2020 IEEE 6th International Conference on Computer and Communications (ICCC), IEEE, 2020, pp. 1146-1150.
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There are 76 citations in total.

Details

Primary Language Turkish
Subjects Information Systems (Other)
Journal Section Review
Authors

Kadir Tohma 0000-0002-2631-7810

Halil İbrahim Okur 0000-0003-0339-4626

Submission Date January 17, 2025
Acceptance Date March 12, 2025
Early Pub Date June 11, 2025
Publication Date June 26, 2025
Published in Issue Year 2025 Volume: 18 Issue: 1

Cite

IEEE K. Tohma and H. İ. Okur, “Ortak Türk Abecesi ve Ses Yapısının Doğal Dil İşleme Uygulamaları Üzerindeki Etkisi: Keşifsel bir Derleme ve Gelecek Yönelimler”, Bilgisayar Bilimleri ve Mühendisliği Dergisi, vol. 18, no. 1, pp. 44–56, 2025, doi: 10.54525/bbmd.1622149.