References
- [1] D.S. Park, W. Chan, Y. Zhang, C.C. Chiu, B. Zoph, E.D. Cubuk, and Q.V. Le, “Specaugment: A simple data augmentation method for automatic speech recognition,” 2019. arXiv preprint arXiv:1904.08779. https://doi.org/10.48550/arXiv.1904.08779
- [2] T. Fukuda, O. Ichikawa, and M. Nishimura, “Detecting breathing sounds in realistic Japanese telephone conversations and its application to automatic speech recognition,” Speech Communication, vol. 98, pp. 95–103, Apr. 2018. https://doi.org/10.1016/j.specom.2018.01.008
- [3] M. Wickert, “Real-time digital signal processing using pyaudio\_helper and the ipywidgets,” in Proceedings of the 17th Python in Science Conference, Austin, TX, USA, Jul. 2018, pp. 9–15. https://doi.org/10.25080/Majora-4af1f417-00e
- [4] A. Srivastava and S. Maheshwari, “Signal denoising and multiresolution analysis by discrete wavelet transform,” Innovative Trends in Applied Physical, Chemical, Mathematical Sciences and Emerging Energy Technology for Sustainable Development, 2015.
- [5] J. P. Dron and F. Bolaers, “Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings,” Journal of Sound and Vibration, vol. 270, no. 1–2, pp. 61–73, Feb. 2004. https://doi.org/10.1016/S0022-460X(03)00483-8
- [6] E. Eban, A. Jansen, and S. Chaudhuri, “Filtering wind noises in video content,” U.S. Patent Application 15/826 622, March 22, 2018.
- [7] B.B. Ali, W. Wojcik, O. Mamyrbayev, M. Turdalyuly, and N. Mekebayev, “Speech recognizer -based non-uniform spectral compression for robust MFCC feature extraction,” Przeglad Elektrotechniczny, vol. 94, no. 6, pp.90–93, Jun. 2018. https://doi.org/10.15199/48.2018.06.17
- [8] Ç.P. Dautov and M.S. Özerdem, “Wavelet transform and signal denoising using Wavelet method,” in 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, May 2018, pp. 1–4. https://doi.org/10.1109/SIU.2018.8404418
- [9] R. Liu, L.O. Hall, K.W. Bowyer, D.B. Goldgof, R. Gatenby, and K.B. Ahmed, “Synthetic minority image over-sampling technique: How to improve AUC for glioblastoma patient survival prediction,” in 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada, Oct. 2017, pp. 1357–1362. https://doi.org/10.1109/SMC.2017.8122802
- [10] R. Lotfian and C. Busso, “Over-sampling emotional speech data based on subjective evaluations provided by multiple individuals,” IEEE Transactions on Affective Computing, vol. 12, no. 4, pp. 870–882, Feb. 2019. https://doi.org/10.1109/TAFFC.2019.2901465
- [11] Khan, I., Ullah, A. and Emad, S.M., “Robust Feature Extraction Techniques in Speech Recognition: A Comparative Analysis” in 2019 KIET Journal of Computing and Information Sciences, 2(2), pp.11-11.
- [12] E. Mulyanto, E.M. Yuniarno, and M.H. Purnomo, “Adding an emotions filter to Javanese text -to-speech system,” in 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia, Nov. 2018, pp. 142–146. https://doi.org/10.1109/CENIM.2018.8711229
- [13] H. Liao, G. Pundak, O. Siohan, M.K. Carroll, N. Coccaro, Q.M. Jiang, T.N. Sainath, A. Senior, F. Beaufays, and M. Bacchiani, “Large vocabulary automatic speech recognition for children,” in Sixteenth Annual Conference of the International Speech Communication Association, Dresden, Germany, Sep. 2015, pp. 1611–1615. https://doi.org/10.21437/Interspeech.2015-373
- [14] K.E. Kafoori and S.M. Ahadi, “Robust recognition of noisy speech through partial imputation of missing data,” Circuits, Systems, and Signal Processing, vol. 37, no. 4, pp. 1625–1648, Apr. 2018. https://doi.org/10.1007/s00034-017-0616-4
- [15] H.F.C. Chuctaya, R.N.M. Mercado, and J.J.G. Gaona, “Isolated automatic speech recognition of Quechua numbers using MFCC, DTW and KNN,” Int. J. Adv. Comput. Sci. Appl, vol. 9, no. 10, pp. 24–29, 2018. https://doi.org/10.14569/IJACSA.2018.091003
- [16] A. Winursito, R. Hidayat, A. Bejo, and M.N.Y. Utomo, “Feature data reduction of MFCC using PCA and SVD in speech recognition system,” in 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), Shah Alam, Malaysia, Jul. 2018, pp. 1–6. https://doi.org/10.1109/ICSCEE.2018.8538414
- [17] L.N. Thu, A. Win, and H.N. Oo, “A review for reduction of noise by wavelet transform in audio signals,” International Research Journal of Engineering and Technology (IRJET), vol. 6, no. 5, May 2019.
- [18] Y. Luo and N. Mesgarani, “Tasnet: time-domain audio separation network for real-time, single-channel speech separation,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, Apr. 2018, pp. 696–700. https://doi.org/10.1109/ICASSP.2018.8462116
- [19] E. Ramentol, Y. Caballero, R. Bello, and F. Herrera, “SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory,” Knowledge and Information Systems, vol. 33, no. 2, pp. 245–265, Nov. 2012. https://doi.org/10.1007/s10115-011-0465-6
- [20] L. Abdi and S. Hashemi, “To combat multi-class imbalanced problems by means of over-sampling techniques,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 1, pp. 238–251, Jul. 2015. https://doi.org/10.1109/TKDE.2015.2458858
- [21] A.E. Martin, “A compositional neural architecture for language,” Journal of Cognitive Neuroscience, vol. 32, no. 8, pp. 1407–1427, Aug. 2020. https://doi.org/10.1162/jocn_a_01552
- [22] S. Böck, F. Korzeniowski, J. Schlüter, F. Krebs, and G. Widmer, “Madmom: A new Python audio and music signal processing library,” in Proceedings of the 24th ACM International Conference on Multimedia, Oct. 2016, pp. 1174–1178. https://doi.org/10.1145/2964284.2973795
- [23] Z. Wang, “Baidu online network technology Beijing Co Ltd, Audio processing method and apparatus based on artificial intelligence,” U.S. Patent Application 10/192163, 2019.
- [24] J.P. Cunningham and Z. Ghahramani, “Linear dimensionality reduction: Survey, insights, and generalizations,” The Journal of Machine Learning Research, vol. 16, no. 1, pp. 2859–2900, 2015. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://stat.columbia.edu/~cunningham/pdf/CunninghamJMLR2015.pdf
- [25] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp.436–444, May 2015. https://doi.org/10.1038/nature14539
- [26] W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, Mar. 2016, pp. 4960–4964. https://doi.org/10.1109/ICASSP.2016.7472621
- [27] J.K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” in Advances in Neural Information Processing Systems, Proceedings of the Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada, 2015, pp. 577–585. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://proceedings.neurips.cc/paper/2015/file/1068c6e4c8051cfd4e9ea8072e3189e2-Paper.pdf
- [28] V.Z. Këpuska and H.A. Elharati, “Robust speech recognition system using conventional and hybrid features of MFCC, LPCC, PLP, RASTAPLP and hidden Markov model classifier in noisy conditions,” Journal of Computer and Communications, vol. 3, no. 6, pp. 1–9, Jun. 2015. https://doi.org/10.4236/jcc.2015.36001
- [29] Ç.P. Dautov and M.S. Özerdem, “Wavelet transform and signal denoising using Wavelet method,” in 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, May 2018, pp. 1–4. https://doi.org/10.1109/SIU.2018.8404418
- [30] N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002. https://doi.org/10.1613/jair.953
- [31] Z. Tüske, R. Schlüter, and H. Ney, “Acoustic modeling of speech waveform based on multi-resolution, neural network signal processing,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, Apr. 2018, pp. 4859–4863. https://doi.org/10.1109/ICASSP.2018.8461871
- [32] R. Shadiev, T.T. Wu, A. Sun, and Y.M. Huang, “Applications of speech-to-text recognition and computer-aided translation for facilitating cross-cultural learning through a learning activity: issues and their solutions,” Educational Technology Research and Development, vol. 66, no. 1, pp. 191–214, Feb. 2018. https://doi.org/10.1007/s11423-017-9556-8