Robust speech recognition based on deep learning for sports game review

[1] Daga, N., Deole, P. Y., Chopdekar, S. (2021). Real time transcription and feed of voice messages based on user presence and preference. US20210306294A1. Search in Google Scholar

[2] Saleem, N., Gao, J., Khattak, M. I., Rauf, H. T., Kadry, S., & Shafi, M. (2022). Deepresgru: residual gated recurrent neural network-augmented kalman filtering for speech enhancement and recognition. Knowledge-Based Systems, 238, 107914. Search in Google Scholar

[3] Wang, Z., Wang, H., Yu, H., et al. (2021). Interaction With Gaze, Gesture, and Speech in a Flexibly Configurable Augmented Reality System. IEEE transactions on human-machine systems, 51-5. Search in Google Scholar

[4] Lin, Y., Wu, Y. K., Guo, D., et al. (2021). A Deep Learning Framework of Autonomous Pilot Agent for Air Traffic Controller Training. IEEE transactions on human-machine systems, 51-5. Search in Google Scholar

[5] Yamauchi, A., Imagawa, H., Yokonishi, H., et al. (2022). Gender- and Age- Stratified Normative Voice Data in Japanese-Speaking Subjects: Analysis of Sustained Habitual Phonations. Search in Google Scholar

[6] Xie, Q., Kim, Y., Wang, Y., et al. (2014). Principles and Efficient Implementation of Charge Replacement in Hybrid Electrical Energy Storage Systems. IEEE Transactions on Power Electronics, 29-11. Search in Google Scholar

[7] Schimmels, J. E. (2020). Update on ART (Accelerated Resolution Therapy) in the Military and Beyond. Journal of the American Psychiatric Nurses Association, (4)26. Search in Google Scholar

[8] Hasan, R., Shams, R., Rahman, M., et al. (2021). Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri. Search in Google Scholar

[9] Choi, W. Y., Lee, S. H., Chung C. C. (2022). Horizonwise Model-Predictive Control With Application to Autonomous Driving Vehicle. IEEE transactions on industrial informatics, 18-10. Search in Google Scholar

[10] Wang, Z., Wang, H., Yu, H., et al. (2021). Interaction With Gaze, Gesture, and Speech in a Flexibly Configurable Augmented Reality System. IEEE transactions on human-machine systems, 51-5. Search in Google Scholar

[11] Chen, J., Wang, Y., Yoho, S. E., Wang, D., & Healy, E. W. (2016). Large-scale training to increase speech intelligibility for hearing-impaired listeners in novel noises. The Journal of the Acoustical Society of America, 139(5), 2604-2612. Search in Google Scholar

[12] Mimura, M., Sakai, S., Kawahara, T. (2016). Joint optimization of denoising autoencoder and DNN acoustic model based on multi-target learning for noisy speech recognition. Proceedings of the 17th Annual Conference of the International Speech Communication Association, 3803-3807. Search in Google Scholar

[13] Wang, Z. Q., & Wang, D. (2016). A joint training framework for robust automatic speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(4), 796-806. Search in Google Scholar

[14] Ravanelli, M., Brakel, P., Omologo, M., et al. (2017). A network of deep neural networks for distant speech recognition. Proceedings of the 42th IEEE International Conference on Acoustics, Speech and Signal Processing, 4880-4884. Search in Google Scholar

[15] Huang, P. S, Kim, M., Hasegawa-Johnson, M., et al. (2014). Deep learning for monaural speech separation. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1562-1566. Search in Google Scholar

[16] Huang, P. S., Kim, M., Hasegawa-Johnson, M., et al. (2015). Joint optimization of masks and deep recurrent neural networks for monaural source separation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(12), 2136-2147. Search in Google Scholar

[17] Geiger, J. T., Weninger, F., Gemmeke, J. F., et al. (2014). Memory-enhanced neural networks and NMF for robust ASR. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 22(6), 1037-1046. Search in Google Scholar

[18] Chan, W., Jaitly, N., Le, Q., et al. (2016). Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. Proceedings of the 2016 International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 4960–4964. Search in Google Scholar

[19] Zhang, Z.,Geiger, J., Pohjalainen, J., et al. (2018). Deep learning for environmentally robust speech recognition: An overview of recent developments. ACM Transactions on Intelligent Systems and Technology, 9(5), 49:1-49:28. Search in Google Scholar

[20] Gupta, S., Nguyen, D., Rana, S., et al. (2022). Verification of integrity of deployed deep learning models using Bayesian Optimization. Knowledge-based systems, 241-Apr.6. Search in Google Scholar

[21] Kang, S., Han, D., Lee, J., et al. (2021). GANPU: An Energy-Efficient Multi-DNN Training Processor for GANs With Speculative Dual-Sparsity Exploitation. IEEE Journal of Solid-State Circuits, 56-9. Search in Google Scholar

[22] Hormaechea-Agulla, D., Matatall, K.. A., L,e D. T., et al. (2021). Article Chronic infection drives Dnmt3a-loss-of-function clonal hematopoiesis via IFN gamma signaling. Cell stem cell, 28-8. Search in Google Scholar

[23] Hk, A., Ja, B., Mk, C. (2022). An Improved Method for Text Detection using Adam Optimization Algorithm. Global Transitions Proceedings, 23-8, 112-145. Search in Google Scholar

[24] BaiI, C. T., Gao, Z. Q., Li A., et al. (2021). Research on speech recognition of military equipment control based on gateway network. Journal of Computer Engineering, 47(7), 301-306. Search in Google Scholar

[25] Zhao, X., Shao, Y., Wang, D. (2012). CASA-based robust speaker identification. IEEE Transactions on Audio, Speech, and Language Processing, IEEE, 20(5), 1608–1616. Search in Google Scholar

[26] Dauphin, Y. N., Fan A., Auli M., et al. (2017). Language modeling with gated convolutional networks. Proceedings of the 2017 International conference on machine learning. PMLR, 933–941. Search in Google Scholar

[27] Ravanelli, M., Zhong, J., Pascual, S., et al. (2020). Multi-task self -supervised learning for robust speech recognition. Proceedings of the 2020 International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 6989–6993. Search in Google Scholar

[28] He, K, Zhang, X., Ren, S., et al. (2016). Deep residual learning for image recognition. Proceedings of the 2016 International Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 770–778. Search in Google Scholar

[29] Bu,, H., Du J., Na, X., et al. (2017). Aishell-1: An open-source mandarin speech corpus and a speech recognition baseline. Proceedings of the 2017 Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA). Piscataway: IEEE, 1–5. Search in Google Scholar

[30] Kim,, S., Hori, T., Watanabe S. (2017). Joint CTC-attention based end-to-end speech recognition using multi-task learning. Proceedings of the 2017 International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 4835–4839. Search in Google Scholar

[31] Ravi, M. (2020). Distribution of a codeword across individual storage units to reduce the bit error rate:, EP3699762A1, 135–143. Search in Google Scholar

[32] Sabir, Z., Raja, M. A. Z., Guirao, J. L. G., et al. (2021). A novel design of fractional Meyer wavelet neural networks with application to the nonlinear singular fractional Lane-Emden systems. Alexandria Engineering Journal, 60(2), 2641-2659. Search in Google Scholar