Investigating the Effectiveness, the Intelligent Speech Recognition System in Correctly Retrieving Narrative Literature Information

Document Type : Research Paper

Authors

1 Department of Knowledge and Information Science, Shahid Bahonar University of Kerman, Kerman, Iran.

2 Department of Persian Literature and Language, Shahid Bahonar University of Kerman, Kerman, Iran.

Abstract

Objective: This research has been carried out with the aim of investigating the effectiveness of using the intelligent speech recognition system in correctly retrieving the information of narrative literature for elementary school children.
Methodology: This research is practical in terms of purpose and was done with a semi-experimental method. The statistical population of the research consists of first to third grade elementary school children, 36 of whom were selected as a sample using stratified sampling method. In order to conduct this study, two intelligent speech recognition system tools and a storytelling application called "Audio Story Book" were used. To collect the data, a questionnaire and a checklist designed according to the research questions. The questionnaire was used to complete the survey and evaluate the determined criteria. SPSS27 statistical software and one-way analysis of variance statistical test were used to analyze the data.
Results: The findings of this research showed that the average number of errors in the story information retrieved by the children of the intelligent speech recognition system group with an average of 0.7 is more than the human voice group and less than the storytelling application group. The average length of story information retrieved by the children of the intelligent speech recognition system group with an average of 3.966 is less than the human voice group and more than the storytelling application group. The average speed of retrieving story information by children in the intelligent speech system group with an average of 2.583 is lower than the human voice group and higher than the storytelling application group. The average accuracy of story information retrieved by the speech recognition system group children is lower than the human voice group and higher than the storytelling application group with an average of 3.4.
Conclusions: The results of this research showed that the use of intelligent speech recognition system is not significantly effective in correctly retrieving the information of narrative literature of primary school children. Children who were told stories by a human had higher accuracy and concentration, and this could be due to physical, emotional, eye, face-to-face communication and environmental conditions that affected their psyche and increased memory and learning performance, increased attention and concentration, quick processing of information, perceptual organization and recall of information, which led to their complete understanding and comprehension.

Keywords


Iranmanesh, Z. (2019). The effect of fiction on children's development and thinking. Ourmazd, 47, 31-47. (In Persian)
Bahrani, M., & Sameti, H. (2010). Using linguistic information in a Persian continuous speech recognition system. Language and Linguistics, 11, 87-112. (In Persian)
Bhukya, S. (2018). Effect of gender on improving speech recognition system. International Journal of Computer Applications, 179 (14), 22-30.
Diyanat, R., Ali Ahmadi, M., Akhlaghi, M. Y., & Baba Ali, B. (2016). Presenting a new method of retrieving suitable information for texts obtained from speech recognition. Signal and Data Processing Quarterly, 30, 93-108. (In Persian)
Elavarasi, S., & Suseendran, G. (2020). Automatic robot processing using speech recognition system. Department of Information and Technology, School of Computing Sciences, Vels Institute of Science Technology & Advanced Studies, 185-195. https://doi.org/10.1007/978-981-32-9949-8_14
Erratahi, R., El Hannani, A., & Ouahmane, H. (2018). Automatic speech recognition errors detection and correction: A review. Procedia Computer Science, 128, 32-37. https://doi.org/10.1016/j.procs.2018.03.005
Godara, S. (2019). Speech recognition using machine learning: a review. International Journal of Electronics Engineering, 11 (1), 971-976.
Haji Nasrallah, S. (2016). Understanding children's literature. Tehran: Ministry of Culture and Islamic Guidance. (In Persian)
Hasani, M. (2018). A new method for speech noise reduction and accurate speech recognition using probabilistic models for clean speech and noise. Sama Technical and Vocational School, Master thesis, Islamic Azad University, Babol, Iran. (In Persian)
Iranmanesh, Z. (2018). The effect of fiction on children's development and thinking. Urmazd Quarterly, 47, 28-41. (In Persian)
Jahangeshte, I., Dusti, A. R., & Dehghani, M. (2021). Emotion recognition by speech processing and feature selection. The 9th National Conference of Applied Researches in Electrical Sciences, Computers and Medical Engineering. (In Persian)
Jia, Y., Hong, M., Hou, J., Ren, K., Ma, S., Wang, J., & Wang, J. (2022, December). LeVoice ASR systems for the ISCSLP 2022 intelligent cockpit speech recognition challenge. In 2022 13th International Symposium on Chinese Spoken Language Processing (ISCSLP) (pp. 517-521). IEEE. https://doi.org/10.1109/ISCSLP57327.2022.10038155
Kagolovsky, Y., & Moehr, J. R. (2003). Terminological problems in information retrieval. Journal of Medical Systems, 27(5), 399-408. https://doi.org/10.1023/A:1025687220609
Kamali, M., & Sheikh Taheri, A. (2018). Documentation of nursing reports using speech recognition technology (advantages, obstacles and challenges and facilitators). Journal of Health and Biomedical Informatics, 5(1), 72-80. (In Persian)
Katyal, A., Kaur, A., & Gill, J. (2014). Automatic speech recognition: a review. International Journal of Engineering and Advanced Technology, 3(3), 71-74. https://doi.org/10.1007/1-4020-2673-0_3
Liu, Y., Li, H., Liang, X., Deng, H., Zhang, X., Heidari, H, & Zhang, X. (2023). Speech Recognition Using Intelligent Piezoresistive Sensor Based on Polystyrene Sphere Microstructures. Advanced Intelligent Systems, 2200427. https://doi.org/10.1002/aisy.202200427
Mahmood, A., & Kose, U. (2021). Speech recognition based on convolutional neural networks and MFCC algorithm. Advances in Artificial Intelligence Research, 1(1), 6-12. https://doi.org/10.3390/bdcc7030132
Mahrad, J., & Coleinney, S. (2007). Investigating the vector space model in information retrieval. Library and Information Quarterly, 10(2), 198-210.
Maning, C., Raghavan, P., & Schutze, H. (2009). An introduction to information retrieval. Cambridge University Press, p. 569.
Matalebi, M., & Bastan Fard, A. (2017). Overview of voice recognition systems, concepts and methods. The third international conference on information technology, computer engineering and telecommunications. (In Persian)
Prodeus, A., & Kukharicheva, K. (2017). Automatic speech recognition performance for training on noised speech. International Conference Advanced Information and Communication Technologies, pp. 1-4. https://doi.org/10.1109/AIACT.2017.8020068
Purdaryai, A., & Saidi, S. (2012). What is literature? The 7th Persian Language and Literature Research Conference, pp.283-291. (In Persian)
Purebrahim, Y., Razazi, F., & Sameti, H. (2021). Recognition of emotions from speech using a combination of transformer and convolutional neural networks. Smart methods in the electricity industry, 13(52), 79-98. (In Persian)
Shahbazi, M. (2012). Library materials and services for children and teenagers. Tehran: Payam Noor University. (In Persian)
Vimala, C., & Radha, D. V. (2012). A review on speech recognition challenges and approaches. World of Computer Science and Information Technology Journal, 2 (1), 1-7. http://dx.doi.org/10.5120/20284-2839
Zerari, N., Yousfi, B., & Abdelhamid, S. (2016). Automatic speech recognition: a review. International Academic Research Journal of Business and Technology, 2(2), 63-68. https://doi.org/10.1007/1-4020-2673-0_3