Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891994948> ?p ?o ?g. }
Showing items 1 to 83 of
83
with 100 items per page.
- W2891994948 abstract "Speech emotion recognition is a difficult task in the field of affective computing because emotions in speech heavily depend on a variety of factors such as feeling, thought, behaviour, mood, temperament, personality and disposition that are hard to model. Emotion plays a significant role in decision making and it influences human perception, learning, behaviour and relationships between individuals. Gender voice is a contributing factor in boosting the accuracy of emotion recognition systems using speech signals. In this paper, we propose a gender voice recognition method which makes use of feature selection through the Random Forest Recursive Feature Elimination (RF-RFE) algorithm with Gradient Boosting Machines (GBMs) algorithm for gender classification. The training and testing data were obtained from a public gender voice dataset. The GBMs algorithm was later evaluated against the feed forward neural network and extreme machine learning algorithms. The classification accuracy of the GBMs improved after applying the RF-RFE to the dataset. Experimental results indicate that GBMs outperformed all the comparative algorithms in classification accuracy and proved to be a suitable candidate for gender voice recognition." @default.
- W2891994948 created "2018-09-27" @default.
- W2891994948 creator A5050825757 @default.
- W2891994948 creator A5061954442 @default.
- W2891994948 date "2018-08-01" @default.
- W2891994948 modified "2023-10-17" @default.
- W2891994948 title "Gender Voice Recognition Using Random Forest Recursive Feature Elimination with Gradient Boosting Machines" @default.
- W2891994948 cites W1678356000 @default.
- W2891994948 cites W2004945478 @default.
- W2891994948 cites W2042276900 @default.
- W2891994948 cites W2051135252 @default.
- W2891994948 cites W2054530658 @default.
- W2891994948 cites W2059673175 @default.
- W2891994948 cites W2059974108 @default.
- W2891994948 cites W2062291211 @default.
- W2891994948 cites W2063125572 @default.
- W2891994948 cites W2068608907 @default.
- W2891994948 cites W2082885908 @default.
- W2891994948 cites W2088794999 @default.
- W2891994948 cites W2096525100 @default.
- W2891994948 cites W2135731872 @default.
- W2891994948 cites W2143426320 @default.
- W2891994948 cites W2165725812 @default.
- W2891994948 cites W2185383584 @default.
- W2891994948 cites W2266199501 @default.
- W2891994948 cites W2310273570 @default.
- W2891994948 cites W2477254990 @default.
- W2891994948 cites W2610156483 @default.
- W2891994948 cites W2621020424 @default.
- W2891994948 doi "https://doi.org/10.1109/icabcd.2018.8465466" @default.
- W2891994948 hasPublicationYear "2018" @default.
- W2891994948 type Work @default.
- W2891994948 sameAs 2891994948 @default.
- W2891994948 citedByCount "21" @default.
- W2891994948 countsByYear W28919949482019 @default.
- W2891994948 countsByYear W28919949482020 @default.
- W2891994948 countsByYear W28919949482021 @default.
- W2891994948 countsByYear W28919949482022 @default.
- W2891994948 countsByYear W28919949482023 @default.
- W2891994948 crossrefType "proceedings-article" @default.
- W2891994948 hasAuthorship W2891994948A5050825757 @default.
- W2891994948 hasAuthorship W2891994948A5061954442 @default.
- W2891994948 hasConcept C119857082 @default.
- W2891994948 hasConcept C138885662 @default.
- W2891994948 hasConcept C153180895 @default.
- W2891994948 hasConcept C154945302 @default.
- W2891994948 hasConcept C169258074 @default.
- W2891994948 hasConcept C2776401178 @default.
- W2891994948 hasConcept C28490314 @default.
- W2891994948 hasConcept C41008148 @default.
- W2891994948 hasConcept C41895202 @default.
- W2891994948 hasConcept C46686674 @default.
- W2891994948 hasConcept C52622490 @default.
- W2891994948 hasConcept C70153297 @default.
- W2891994948 hasConceptScore W2891994948C119857082 @default.
- W2891994948 hasConceptScore W2891994948C138885662 @default.
- W2891994948 hasConceptScore W2891994948C153180895 @default.
- W2891994948 hasConceptScore W2891994948C154945302 @default.
- W2891994948 hasConceptScore W2891994948C169258074 @default.
- W2891994948 hasConceptScore W2891994948C2776401178 @default.
- W2891994948 hasConceptScore W2891994948C28490314 @default.
- W2891994948 hasConceptScore W2891994948C41008148 @default.
- W2891994948 hasConceptScore W2891994948C41895202 @default.
- W2891994948 hasConceptScore W2891994948C46686674 @default.
- W2891994948 hasConceptScore W2891994948C52622490 @default.
- W2891994948 hasConceptScore W2891994948C70153297 @default.
- W2891994948 hasLocation W28919949481 @default.
- W2891994948 hasOpenAccess W2891994948 @default.
- W2891994948 hasPrimaryLocation W28919949481 @default.
- W2891994948 hasRelatedWork W3100297620 @default.
- W2891994948 hasRelatedWork W3200719183 @default.
- W2891994948 hasRelatedWork W3208169454 @default.
- W2891994948 hasRelatedWork W3211193619 @default.
- W2891994948 hasRelatedWork W3212730154 @default.
- W2891994948 hasRelatedWork W4200057378 @default.
- W2891994948 hasRelatedWork W4220785415 @default.
- W2891994948 hasRelatedWork W4283312409 @default.
- W2891994948 hasRelatedWork W4288057626 @default.
- W2891994948 hasRelatedWork W4293069612 @default.
- W2891994948 isParatext "false" @default.
- W2891994948 isRetracted "false" @default.
- W2891994948 magId "2891994948" @default.
- W2891994948 workType "article" @default.