{"id":7372,"date":"2026-05-27T18:41:58","date_gmt":"2026-05-27T10:41:58","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=7372"},"modified":"2026-05-27T20:59:49","modified_gmt":"2026-05-27T12:59:49","slug":"jase-202609-32-057","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-057","title":{"rendered":"Research on hydrodynamic simulation and speech recognition method based on PLDA model"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Wu Lixian1<\/sup><\/i><\/a>, Lai Weiwei2<\/sup>, Bu li1<\/sup>, Lin Yujie1<\/sup>, Wu Guangcai2<\/sup>, and Zheng Yinglong2<\/sup><\/p>\n\n\n\n

1<\/sup>Foshan Power Supply Bureau of Guangdong Power Grid Co., Ltd., Foshan 528000, Guangdong, China<\/p>\n\n\n\n

2<\/sup>Guangdong Electric Power Information Technology Co., Ltd., Guangzhou 510062, Guangdong, China<\/p>\n<\/div>\n\n\n\n

\n

Received: March 25, 2026
Accepted: April 28, 2026
Publication Date: May 27, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Speech recognition framework diagram <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.057<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

Speech recognition is one of the important technologies of biological information recognition, which can extract the corresponding characteristics through language recognition, so as to recognize the generated speech. The International Institute of Technology (National Institute of Standards and Technology) evaluated speech recognition technology and found that the Probabilistic Linear Discriminant (PLDA), Analysis) model was good However, in practice, speech recognition is easily affected by external factors such as environmental noise and human voice, leading to differences between registered and test speech and making data processing difficult. These challenges limit its application. To address issues of time length variation and limited training samples, this study applies and refines a PLDA-based probabilistic correction model by adjusting its distribution using speech data. Finally, the PLDA parameters obtained by the training are taken as the test value, and the hydrodynamic simulation software is used to improve the performance of speech recognition. Hydrodynamic simulation enhances the PLDA model\u2019s robustness by addressing speech duration and cross-domain variability, leading to improved recognition accuracy. Experiments demonstrate notable gains in both EER and DCF metrics. It is found that the speech recognition method based on PLDA model can effectively improve the recognition function after hydrodynamic simulation, solve the problems of time length mismatch and limited training samples, and provide a theoretical basis for the application of PLDA model in speech recognition.<\/p>\n\n\n\n

Keywords: PLDA model; speech recognition; hydrodynamic simulation; cross-domain migration<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
\n
\n
    \n
  1. [1] H. Lauren, (2023) \u201cIncorporating Automatic Speech Recognition Methods into the Transcription of Police-Suspect Interviews: Factors Affecting Automatic Performance\u201d Frontiers in Communication 8: DOI: 10.3389\/fcomm.2023.1165233.<\/li>\n
  2. [2] Z. Dong, Q. Ding, W. Zhai, and M. Zhou, (2023) \u201cA Speech Recognition Method Based on Domain-Specific Datasets and Confidence Decision Networks\u201d Sensors 23(13): DOI: 10.3390\/s23136036.<\/li>\n
  3. [3] H. Wang, Z. Li, D. Song, X. He, and M. Khan, (2022) \u201cApplying Machine Learning and Automatic Speech Recognition for Intelligent Evaluation of Coal Failure Probability under Uniaxial Compression\u201d Minerals 12(12): 1548. DOI: 10.3390\/min12121548.<\/li>\n
  4. [4] J. Wu, Y. Zhang, L. Xie, Y. Yan, X. Zhang, S. Liu, X. An, E. Yin, and D. Ming, (2022) \u201cA Novel Silent Speech Recognition Approach Based on Parallel Inception Convolutional Neural Network and Mel Frequency Spectral Coefficient\u201d Frontiers in Neurorobotics 16: 971446. DOI: 10.3389\/fnbot.2022.971446.<\/li>\n
  5. [5] M. Amini, D. Matrouf, J. F. Bonastre, S. Dowerah, R. Serizel, and D. Jouvet. \u201cLearning Noise Robust ResNet-Based Speaker Embedding for Speaker Recognition\u201d. In: Proceedings of Odyssey: The Speaker and Language Recognition Workshop. 2022. DOI: 10.21437\/Odyssey.2022-6.<\/li>\n
  6. [6] E. H. Alkhammash, M. Hadjouni, and A. M. Elshewey, (2022) \u201cA Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach\u201d Electronics 11(11): 1750. DOI: 10.3390\/electronics11111750.<\/li>\n
  7. [7] J. Heeren, T. Nuesse, M. Latzel, I. Holube, V. Hohmann, K. C. Wagener, and M. Schulte, (2022) \u201cThe Concurrent OLSA Test: A Method for Speech Recognition in Multi-talker Situations at Fixed SNR\u201d Trends in Hearing 26: 23312165221108257. DOI: 10.1177\/23312165221108257.<\/li>\n
  8. [8] K. Zhao and D. Wang, (2021) \u201cResearch on Speech Recognition Method in Multi-Layer Perceptual Network Environment\u201d International Journal of Circuits, Systems and Signal Processing 15: 996\u20131004. DOI: 10.46300\/9106.2021.15.107.<\/li>\n
  9. [9] J. Guglani and A. Mishra, (2020) \u201cAutomatic Speech Recognition System with Pitch-Dependent Features for Punjabi Language on KALDI Toolkit\u201d Applied Acoustics 167: DOI: 10.1016\/j.apacoust.2020.107386.<\/li>\n
  10. [10] Y. H. Tu, J. Du, and C. H. Lee, (2019) \u201cSpeech Enhancement Based on Teacher\u2013Student Deep Learning Using Improved Speech Presence Probability for Noise-Robust Speech Recognition\u201d IEEE\/ACM Transactions on Audio, Speech, and Language Processing 27(12): 2080\u20132091. DOI: 10.1109\/TASLP.2019.2940662.<\/li>\n
  11. [11] H. B. Prasetio, H. Tamura, and K. Tanno, (2019) \u201cGeneralized Discriminant Methods for Improved X-Vector Back-end Based Stress Speech Recognition\u201d IEEJ Transactions on Electronics, Information and Systems 139(11): 1341\u20131347. DOI: 10.1541\/ieejeiss.139.1341.<\/li>\n
  12. [12] T. N. Sainath, R. J. Weiss, K. W. Wilson, A. Narayanan, and M. Bacchiani. \u201cLearning the Speech Front-End with Raw Waveform CLDNNs\u201d. In: Interspeech. 2015, 1\u20135. DOI: 10.21437\/Interspeech.2015-1.<\/li>\n
  13. [13] S. Cumani, O. Plchot, and P. Laface. \u201cProbabilistic Linear Discriminant Analysis of i-Vector Posterior Distributions\u201d. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2013, 7644\u20137648. DOI: 10.1109\/ICASSP.2013.6639150.<\/li>\n
  14. [14] J. Guglani and A. Mishra, (2020) \u201cAutomatic Speech Recognition System with Pitch-Dependent Features for Punjabi Language on KALDI Toolkit\u201d Applied Acoustics 167: DOI: 10.1016\/j.apacoust.2020.107386.<\/li>\n
  15. [15] S. H. Grandhi and M. M. Kamruzzaman, (2024) \u201cAutomatic Stutter Speech Recognition and Classification Using Hyper-Heuristic Search Algorithm\u201d International Journal of Automation and Smart Technology 14(1): DOI: 10.5875\/at.dm761m36.<\/li>\n
  16. [16] X. Chen, X. Liu, Y. Wang, A. Ragni, J. H. Wong, and M. J. Gales, (2019) \u201cExploiting Future Word Contexts in Neural Network Language Models for Speech Recognition\u201d IEEE\/ACM Transactions on Audio, Speech, and Language Processing 27(9): 1444\u20131454. DOI: 10.1109\/TASLP.2019.2922048.<\/li>\n
  17. [17] Y. Zhang, P. Zhang, and Y. Yan, (2019) \u201cLanguage Model Score Regularization for Speech Recognition\u201d Chinese Journal of Electronics 28(3): 604\u2013609. DOI: 10.1049\/cje.2019.03.015.<\/li>\n
  18. [18] C. Li, H. Ge, and G. Chen. \u201cA Robust Speech Feature Extraction Method Based on Nonlinear Power Transform Gammachirp Filter\u201d. CN109256127B. 2021.<\/li>\n
  19. [19] N. Br\u00fcmmer, A. Swart, L. Mo\u0161ner, A. Silnova, O. Plchot, T. Stafylakis, and L. Burget, (2022) \u201cProbabilistic Spherical Discriminant Analysis: An Alternative to PLDA for Length-Normalized Embeddings\u201d arXiv preprint: DOI: 10.48550\/arXiv.2203.14893.<\/li>\n
  20. [20] B. Priyanka and S. I. A., (2018) \u201cA Probabilistic Feature-Based SVM Model for Hindi\/English Speech Recognition\u201d International Journal of Engineering & Technology 7(2.8): 271. DOI: 10.14419\/ijet.v7i2.8.10423.<\/li>\n
  21. [21] V. R. Raman and G. J. Vysotsky. \u201cMethods and Apparatus for Generating and Using Garbage Models for Speaker Dependent Speech Recognition Purposes\u201d. US5842165A. 1998.<\/li>\n
  22. [22] Y. Jeong, (2013) \u201cSpeaker Adaptation Using Probabilistic Linear Discriminant Analysis for Continuous Speech Recognition\u201d Electronics Letters 49(25): 1641\u20131643. DOI: 10.1049\/el.2013.2223.<\/li>\n
  23. [23] C. Zhang, B. H. Roh, and G. Shan. \u201cPoster: Dynamic Clustered Federated Framework for Multi-Domain Network Anomaly Detection\u201d. In: Companion of the 19th International Conference on Emerging Networking Experiments and Technologies. 2023, 71\u201372. DOI: 10.1145\/3624354.3630086.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,720,6],"tags":[1466],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:  BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202609_32.057   Download PDF Speech recognition is one of the important technologies of biological information…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/7372"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7372"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7372"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}