[1] Huang, Z., Epps, J., Joachim, D.: Speech landmark bigrams for depression detection from naturalistic smartphone speech. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5856–5860 (2019)

[2] Anttonen, J., Surakka, V.: Emotions and heart rate while sitting on a chair. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp. 491–499 (2005)

[3] Vogel, H.J., et al.: Emotional awareness for intelligent vehicle assistants: a research agenda. In: IEEE/ACM 1st International Workshop on Software Engineering for AI in Autonomous Systems (SEFAIAS), pp. 11–15 (2018)

[4] Hossain, M.S., Muhammad, G., Song, B., Hassan, M.M., Alelaiwi, A., Alamri, A.: Audio-visual emotion-aware cloud gaming framework. IEEE Trans. Circ. Syst. Video Technol. 25, 2105–2118 (2015)

[5] Badshah, A.M., Ahmad, J., Rahim, N., Baik, S.W.: Speech emotion recognition from spectrograms with deep convolutional neural network. In: 2017 International Conference on Platform Technology and Service (PlatCon) (Busan), pp. 1–5 (2017). https://doi.org/10.1109/PlatCon

[6] Anvarjon, T., Kwon, S.: Deep-Net: a lightweight CNN-based speech emotion recognition system using deep frequency features. Sensors, 5212 (2020)

[7] Kurpukdee, N., Koriyama, T., Kobayashi, T., Kasuriya, S., Wutiwiwatchai, C., Lamsrichan, P.: Speech emotion recognition using convolutional long short-term memory neural network and support vector machines. In: 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1744–1749 (2017)

[8] Satt, A., Rozenberg, S., Hoory, R.: Efficient emotion recognition from speech using deep learning on spectrograms. In: Proceedings of the INTERSPEECH, pp. 1089–1093 (2017)

[9] Etienne, C., Fidanza, G., Petrovskii, A., Devillers, L., Schmauch, B.: CNN+ LSTM architecture for speech emotion recognition with data augmentation. arXiv preprint arXiv:1802.05630 (2018)

[10] Pappagari, R., Wang, T., Villalba, J., Chen, N., Dehak, N.: X-vectors meet emotions: a study on dependencies between emotion and speaker recognition. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7169–7173 (2020)

[11] Lakomkin, E., Zamani, M.A., Weber, C., Magg, S., Wermter, S.: On the robustness of speech emotion recognition for human-robot interaction with deep neural networks. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 854–860 (2018)

[12] Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 936–944 (2017)

[13] Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

[14] Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999–3007 (2017)

[15] Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. ArXiv abs/1704.04861 (2017)

[16] Nediyanchath, A., Paramasivam, P., Yenigalla, P.: Multi-head attention for speech emotion recognition with auxiliary learning of gender recognition. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020)

[17] Keren, G., Schuller, B.: Convolutional RNN: an enhanced model for extracting features from sequential data. In: Proceedings of the IEEE IJCNN, pp. 3412–3419 (2016)

[18] Xie, Y., Liang, R., Liang, Z., Zhao, L.: Attention-based dense LSTM for speech emotion recognition. In: IEICE Trans. Inf. Syst. 1426–1429 (2019)

[19] Ma, X., Wu, Z., Jia, J., Xu, M., Meng, H., Cai, L.: INTERSPEECH, pp. 3683–3687 (2018)

[20] Chen, M., He, X., Yang, J., Zhang, H.: 3-D convolutional recurrent neural networks with attention model for speech emotion recognition. IEEE Signal Process. Lett. 1440–1444 (2018)

[21] Lee, J., Tashev, I.: High-level feature representation using recurrent neural network for speech emotion recognition. In: INTERSPEECH (2015)

[22] Peng, Z., Lu, Y., Pan, S., Liu, Y.: Efficient speech emotion recognition using multi-scale CNN and attention. In: ICASSP (2021)

[23] Vu, T.H., Dang, A., Wang, J.C.: Learning to remember beauty products. In: Proceedings of the 28th ACM International Conference on Multimedia (2020)

[24] Dang, A., Vu, T.H., Wang, J.C.: EMIX: a data augmentation method for speech emotion recognition. In: ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2023)