A ROBUST ENSEMBLE MODEL FOR SPOKEN LANGUAGE RECOGNITION

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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.

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Woods, N., & Babatunde, G. (2020). A robust ensemble model for Spoken Language Recognition. Applied Computer Science, 16(3), 56-68. doi:10.23743/acs-2020-21
Woods, Nancy, and Gideon Babatunde. "A Robust Ensemble Model for Spoken Language Recognition." Applied Computer Science 16, no. 3 (2020): 56-68.
N. Woods and G. Babatunde, "A robust ensemble model for Spoken Language Recognition," Applied Computer Science, vol. 16, no. 3, pp. 56-68, 2020, doi: 10.23743/acs-2020-21.
Woods N, Babatunde G. A robust ensemble model for Spoken Language Recognition. Applied Computer Science. 2020;16(3):56-68.