The pronunciation accuracy of language resource library is the key to improve the quality of language resource library. An automatic pronunciation calibration model construction method based on dynamic time normalization algorithm is proposed. By analyzing the dynamic characteristics of the pronunciation of the language resource library, the acquisition model of the pronunciation of the language resource library is constructed, and the pronunciation of the language resource library is obtained. The ambiguity detection method is used to suppress the noise of the pronunciation signal of the language resource library. According to the processing results, the speech interaction method of the language resource library is used to analyze the matching domain of the voice signal obtained by interval uniform sampling, and extract the voice characteristics of the language resource library, Based on the extracted features, the dynamic time normalization algorithm is used to recognize the speech similarity, and the voice signal detection model of the optimal language resource library is established under the given false alarm probability, so as to improve the automatic voice calibration capability of the language resource library within the prior Doppler frequency range. The simulation results show that this method has a high accuracy probability and a low false alarm probability for speech detection in the language resource library, which improves the ability of speech interaction and dynamic feature analysis of the language resource library.

[1] Amini S. and Woolson R., “Small-Sample Properties of Covariance-Adjusted Survivorship Data Tests for Treatment Effect,” Communication in Statistics-Simulation and Computation, vol. 17, no. 4, pp. 1281-1306, 1988. https://doi.org/10.1080/03610918808812725

[2] Chao X., Kuo N., John P., El-Khaissi C., and Suominen H., “An Automatic Vowel Space Generator for Language Learners’ Pronunciation Acquisition and Correction,” in Proceedings of the 18th Workshop of the Australasian Language Technology, Virtual, pp. 54-64, 2020. https://aclanthology.org/2020.alta-1.6/

[3] Chen Y. and Yeh L., “Dynamic Association Between Phonemic Awareness and Disordered Speech Recognition Moderated by Transcription Training,” International Journal of Language and Communication Disorders, vol. 58, no. 6, pp. 2178-2199, 2023. https://doi.org/10.1111/1460- 6984.12933

[4] Chen Y., Zhang J., Yuan X., Zhang S., and et al., “SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition Systems,” ACM Transactions on Privacy and Security, vol. 25, no. 3, pp. 1-31, 2022. https://doi.org/10.1145/3510582

[5] Ciampelli S., Voppel A., Boer J., Koops S., and et al., “Combining Automatic Speech Recognition with Semantic Natural Language Processing in Schizophrenia,” Psychiatry Research, vol. 325, pp. 115252, 2023. https://doi.org/10.1016/j.psychres.2023.115252

[6] Dong H., Ma J., and Zhang C., “Speech Enhancement Based on Time-Domain Waveform Mapping-Frequency-Domain Harmonic Loss,” Computer Engineering and Design, vol. 42, no. 6, pp. 1677-1683, 2021. https://chn.oversea.cnki.net/kcms/detail/detail.asp x?filename=SJSJ202106023&dbcode=CJFQ&db name=CJFDLAST2021&uniplatform=NZKPT

[7] Gao W., “Research on End-to-End Pronunciation Error Detection Based on Transfer Learning,” Computer Science and Application, vol. 11, no. 4, pp. 885-891, 2021. http://dx.doi.org/10.12677/CSA.2021.114091

[8] Jiang M., Jong M., Lau W., Kim S., and et al., “Exploring the Effects of Automatic Speech Recognition Technology on Oral Accuracy and Fluency in a Flipped Classroom,” Journal of Computer Assisted Learning, vol. 39, no. 1, pp. 125-140, 2023. https://doi.org/10.1111/jcal.12732

[9] Jin L., “Research on Pronunciation Accuracy Detection of English Chinese Consecutive Interpretation in English Intelligent Speech Translation Terminal,” International Journal of Speech Technology, vol. 27, pp. 503, 2021. https://doi.org/10.1007/s10772-021-09839-7

[10] Jin Z., Geng M., Xie X., Wang T., and et al., “Adversarial Data Augmentation for Disordered Speech Recognition,” in Proceedings of the IEEE International Conference on Acoustics, Rhodes Island, pp. 4803-4807, 2021. https://doi.org/10.1109/ICASSP49357.2023.1009 5547

[11] Lin Z., Zeng B., Huang Y., Hu H., and et al., “SASE: Self-Adaptive Noise Distribution Network for Speech Enhancement with Federated Learning Using Heterogeneous Data,” Knowledge- Based Systems, vol. 266, no. 22, pp. 1-15, 2023.

[12] Raval D., Pathak V., Patel M., and Bhatt B., “Improving Deep Learning Based Automatic Speech Recognition for Gujarati,” in Proceedings of the ACM Transactions on Asian and Low- Resource Language Information Processing, New York, pp. 1-18, 2022. https://doi.org/10.1145/3483446

[13] Saeli H., Rahmati P., and Dalman M., “Oral Corrective Feedback on Pronunciation Errors: The Mediating Effects of Learners’ Engagement with Feedback,” Advances in Language and Literary Studies, vol. 12, no. 4, pp. 68-78, 2021. https://doi.org/10.7575/aiac.alls.v.12n.4.p.68

[14] Salinas J., Garcia A., Garcia C., and Pineda L., “Intra-Subject Class-Incremental Deep Learning Approach for EEG-Based Imagined Speech Recognition,” Biomedical Signal Processing and Control, vol. 81, no. 3, pp. 1-9, 2023. https://doi.org/10.1016/j.bspc.2022.104433

[15] Shahin M. and Ahmed B., “Anomaly Detection Based Pronunciation Verification Approach Using Speech Attribute Features,” Speech Communication, vol. 111, pp. 29-43, 2019. https://doi.org/10.1016/j.specom.2019.06.003

[16] Sheng Y. and Yang K., “Automatic Correction System Design for English Pronunciation Errors Assisted by High-Sensitivity Acoustic Wave 176 The International Arab Journal of Information Technology, Vol. 23, No. 1, January 2026 Sensor,” Hindawi Limited, vol. 2021, no. 8, pp. 1- 12, 2021. http://dx.doi.org/10.1155/2021/2853056

[17] Shufang Z., “Design of an Automatic English Pronunciation Error Correction System Based on Radio Magnetic Pronunciation Recording Devices,” Journal of Sensors, vol. 2021, no. 11, pp. 1-12, 2021. https://doi.org/10.1155/2021/5946228

[18] Wang S. and Shi X., “Research on Correction Method of Spoken Pronunciation Accuracy of AI Virtual English Reading,” Advances in Multimedia, vol. 2021, no. 1, pp. 1-12, 2021. https://doi.org/10.1155/2021/6783205

[19] Wang Z., Wichern G., Watanabe S., and Roux J., “STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 397-410, 2023. https://doi.org/10.1109/TASLP.2022.3224285

[20] Xiao C. and Chen Y., “Real-time Speech Enhancement Algorithm Based on Cyclic Neural Network,” Computer Engineering and Design, vol. 42, no. 7, pp. 1989-1994, 2021. https://doi.org/10.16208/j.issn1000- 7024.2021.07.026

[21] Yang Y., Lee B., Cho J., Kim S., and et al., “A Digital Capacitive MEMS Microphone for Speech Recognition with Fast Wake-Up Feature Using a Sound Activity Detector,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 9, pp. 1509-1513, 2020. https://doi.org/10.1109/TCSII.2020.3009926

[22] Zhang X., “Automatic Pronunciation Calibration System for Spoken English Based on Speech Perception,” Techniques of Automation and Applications, vol. 42, no. 5, pp. 44-47, 2023.

[23] Zheng R., “Experimental Simulation Analysis of English Pronunciation Calibration Based on Virtual Reality,” Research and Exploration in Laboratory, vol. 42, no. 11, pp. 119-123, 2023. https://doi.org/ 10.19927/j.cnki.syyt.2023.11.024