과제정보
이 논문은 2021년도 세종대학교 교내연구비 지원에 의하여 연구되었음(No.20211105).
참고문헌
- Eunji Lee(2022). ASTI MARKET INSIGHT 67: Speech recognition service.
- Integrated Data Analysis Center, "Development of the world's first 'voice phishing voice analysis model'," Ministry of the Interior and Safety, 2023.02.22.
- AI 기술 및 제품.서비스 개발에 필요한 AI 통합 플랫폼 [Internet], https://aihub.or.kr/
- J. Liu, Z. Liu, L. Wang, L. Guo, and J. Dang, "Speech emotion recognition with local-global aware deep representation learning," In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 7174-7178), IEEE, 2020.
- S. Kwon, "MLT-DNet: Speech emotion recognition using 1D dilated CNN based on multi-learning trick approach," Expert Systems with Applications, Vol.167, pp.114177, 2021.
- M. Ishaq, M. Khan, and S. Kwon, "TC-Net: A modest & lightweight emotion recognition system using temporal convolution network," Computer Systems Science & Engineering, Vol.46, No.3, pp.3355-3369, 2023. https://doi.org/10.32604/csse.2023.037373
- J. Cai, et al., "Feature-level and model-level audiovisual fusion for emotion recognition in the wild," In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp.443-448). IEEE, 2019.
- G. Chen, S. Zhang, X. Tao, and X. Zhao, "Speech emotion recognition by combining a unified first-order attention network with data balance," IEEE Access, Vol.8, pp.215851-215862, 2020. https://doi.org/10.1109/ACCESS.2020.3038493
- S. Zhang, X. Tao, Y. Chuang, and X. Zhao, "Learning deep multimodal affective features for spontaneous speech emotion recognition," Speech Communication, Vol.127, pp.73-81, 2021. https://doi.org/10.1016/j.specom.2020.12.009
- A., Amjad, L., Khan, N., Ashraf, M. B., Mahmood, and H. T. Chang, "Recognizing semi-natural and spontaneous speech emotions using deep neural networks," IEEE Access, Vol.10, pp.37149-37163, 2022.
- Mustaqeem and S. Kwon, "A CNN-assisted enhanced audio signal processing for speech emotion recognition," Sensors, Vol.20, No.1, pp.183, 2019.
- M. Khan, M. Ishaq, M. Swain, and S. Kwon, "Advanced sequence learning approaches for emotion recognition using speech signals," In Intelligent Multimedia Signal Processing for Smart Ecosystems (pp.307-325). Cham: Springer International Publishing, 2023.
- H. C. Shin, et al., "Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning," IEEE Transactions on Medical Imaging, Vol.35, No.5, pp.1285-1298, 2016. https://doi.org/10.1109/TMI.2016.2528162
- O. Russakovsky, et al., "Imagenet large scale visual recognition challenge," International Journal of Computer Vision, Vol.115, pp.211-252, 2015.
- O. Abdel-Hamid, A. R. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, "Convolutional neural networks for speech recognition," IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol.22, No.10, pp.1533-1545, 2014. https://doi.org/10.1109/TASLP.2014.2339736
- A. B. Nassif, I. Shahin, I. Attili, M. Azzeh, and K. Shaalan, "Speech recognition using deep neural networks: A systematic review," IEEE Access, Vol.7, pp.19143-19165, 2019. https://doi.org/10.1109/ACCESS.2019.2896880
- A. Aggarwal, et al., "Two-way feature extraction for speech emotion recognition using deep learning," Sensors, Vol.22, No.6, pp.2378, 2022.
- S. Akinpelu, S. Viriri, and A. Adegun, "Lightweight Deep Learning Framework for Speech Emotion Recognition," IEEE Access, Vol.11, pp.77086-7709, 2023. https://doi.org/10.1109/ACCESS.2023.3297269
- 감정이 태깅된 자유대화(성인) [Internet], https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71631
- 감정이 태깅된 자유대화(청소년) [Internet], https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71632
- M. Abadi, et al., "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," arXiv preprintarXiv: 1603.04467, 2016.
- M. Sokolova, N. Japkowicz, and S. Szpakowicz, "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation," In Australasian Joint Conference on Artificial Intelligence (pp.1015-1021). Berlin, Heidelberg: Springer Berlin Heidelberg, 2006.