A ROBUST ENSEMBLE MODEL FOR SPOKEN LANGUAGE RECOGNITION
Nancy WOODS
chyn.woods@gmail.comUniversity of Ibadan, Faculty of Science, Department of Computer Science, Oyo State Ibadan (Nigeria)
Gideon BABATUNDE
* University of Ibadan, Faculty of Science, Department of Computer Science, Oyo State Ibadan (Nigeria)
Abstract
Effective decision-making in industry conditions requires access and proper presentation of manufacturing data on the realised manufacturing process. Although the frequently applied ERP systems allow for recording economic events, their potential for decision support is limited. The article presents an original system for reporting manufacturing data based on Business Intelligence technology as a support for junior and middle management. As an example a possibility of utilising data from ERP systems to support decision-making in the field of purchases and logistics in small and medium enterprises.
Keywords:
Spoken Language Recognition, Computer Vision, Image Recognition, CNNReferences
Abdel-Hamid, O., Mohamed, A. R., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014). Convolutional neural networks for speech recognition. IEEE Transactions on Audio, Speech and Language Processing, 22(10), 1533–1545. https://doi.org/10.1109/taslp.2014.2339736
DOI: https://doi.org/10.1109/TASLP.2014.2339736
Google Scholar
Adami, A., & Hermansky, H. (2003). Segmentation of speech for speaker and language recognition. EUROSPEECH-2003 (pp. 841–844). Geneva. Retrieved from https://www.academia.edu/32317887/Segmentation_of_speech_for_speaker_and_language_recognition
DOI: https://doi.org/10.21437/Eurospeech.2003-189
Google Scholar
Amodei, D., Anubhai, R., Battenberg, E., Case, C., Casper, J., Catanzaro, B., ... Narang, S. (2015). Deep Speech 2: End-to-End Speech Recognition in English and Mandarin. CoRR, abs/1512.02595. Retrieved from https://arxiv.org/abs/1512.02595v1
Google Scholar
Ashby, M., & Maidment, J. (2005). Introducing phonetic science. Cambridge University Press.
DOI: https://doi.org/10.1017/CBO9780511808852
Google Scholar
Bartz, C., Herold, T., Yang, H., & Meinel, C. (2017). Language Identification Using Deep Convolutional Recurrent Neural Networks. In D. Liu, S. Xie, Y. Li, D. Zhao, & E. El-Alfy (Eds.), Neural Information Processing ICONIP 2017. Lecture Notes in Computer Science (vol. 10639). Springer. https://doi.org/10.1007/978-3-319-70136-3_93
DOI: https://doi.org/10.1007/978-3-319-70136-3_93
Google Scholar
Boussard, J., Deveau, A., & Pyron, J. (2017). Methods for Spoken Language Identification. Retrieved from http://cs229.stanford.edu/proj2017/final-reports/5239784.pdf
Google Scholar
Eberhard, D. M., Simons, G. F., & Fennig, C. D. (Eds.). (2020). Ethnologue: Languages of the World. Retrieved from http://www.ethnologue.com
Google Scholar
Kirchhoff, K. (2006). Language characteristics. In T. Schultz, & K. Kirchhoff (Eds.), Multilingual Speech Processing (pp. 5–33). Elsevier.
DOI: https://doi.org/10.1016/B978-012088501-5/50005-6
Google Scholar
Li, H., Ma, B., & Lee, K. A. (2013). Spoken Language Recognition: From Fundamentals to Practice. Proceedings of the IEEE, 101(5), 1136–1159. https://doi.org/10.1109/JPROC.2012.2237151
DOI: https://doi.org/10.1109/JPROC.2012.2237151
Google Scholar
Muthusamy, Y. K., Cole, R., & Oshika, B. (1992). The OGI multi-language telephone speech corpus. Int. Conf. Spoken Lang. Process, 895-898. Retrieved from https://pdfs.semanticscholar.org/aad7/274fdd57191e89f9df2880a50ec14581d671.pdf
DOI: https://doi.org/10.21437/ICSLP.1992-276
Google Scholar
Navratil, J. (2001). Spoken language recognition A step toward multilinguality in speech processing. IEEE Trans. Speech Audio Process, 9(6), 678–685. https://doi.org/10.1109/89.943345
DOI: https://doi.org/10.1109/89.943345
Google Scholar
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019). SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition. Proc. Interspeech 2019 (pp. 2613–2617). https://doi.org/10.21437/interspeech.2019-2680
DOI: https://doi.org/10.21437/Interspeech.2019-2680
Google Scholar
Ramus, F., & Mehler, J. (1999). Language identification with suprasegmental cues: A study based on speech re-synthesis. Journal of Acoustical Society of America, 105(1), 512–521. https://doi.org/10.1121/1.424522
DOI: https://doi.org/10.1121/1.424522
Google Scholar
Safitri, N. E., Zahra, A., & Adriani, M. (2016). Spoken Language Identification with Phonotactics Methods on Minangkabau, Sundanese, and Javanese Languages. Procedia Computer Science 81 (pp. 182–187). Elsevier. https://doi.org/10.1016/j.procs.2016.04.047
DOI: https://doi.org/10.1016/j.procs.2016.04.047
Google Scholar
Sugiyama, M. (1991). Automatic language recognition using acoustic features. International Conference on Acoustics, Speech, and Signal Processing (pp. 813–816). Toronto. https://doi.org/10.1109/icassp.1991.150461
DOI: https://doi.org/10.1109/ICASSP.1991.150461
Google Scholar
Torres-Carrasquillo, P., Singer, E., Kohler, M., Greene, R., Reynolds, D., & Deller, J. (2002). Approaches to language identification using Gaussian mixture models and shifted delta cepstral features. In ICSLP-2002 (pp. 89–92). Denver. https://doi.org/10.1109/icassp.2002.5743828
DOI: https://doi.org/10.21437/ICSLP.2002-74
Google Scholar
Zhao, J., Shu, H., Zhang, L., Wang, X., Gong, Q., & Li, P. (2008). Cortical competition during language discrimination. NeuroImage, 43(3), 624–633. https://doi.org/10.1016/j.neuroimage.2008.07.025
DOI: https://doi.org/10.1016/j.neuroimage.2008.07.025
Google Scholar
Zissman, M. (1996). Comparison of four approaches to automatic language identification of telephone speech. IEEE Transactions on Speech and Audio Processing, 4(1), 31–44. https://doi.org/10.1109/icassp.1993.319323
DOI: https://doi.org/10.1109/TSA.1996.481450
Google Scholar
Zissman, M. A. (1993). Automatic language identification using Gaussian mixture and hidden Markov models. IEEE International Conference on Acoustics, Speech and Signal Processing (Vol. 2, pp. 399–402). IEEE. https://doi.org/10.1109/tsa.1996.481450
DOI: https://doi.org/10.1109/ICASSP.1993.319323
Google Scholar
Authors
Nancy WOODSchyn.woods@gmail.com
University of Ibadan, Faculty of Science, Department of Computer Science, Oyo State Ibadan Nigeria
Authors
Gideon BABATUNDE* University of Ibadan, Faculty of Science, Department of Computer Science, Oyo State Ibadan Nigeria
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