Matches in SemOpenAlex for { <https://semopenalex.org/work/W4320925651> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W4320925651 endingPage "749" @default.
- W4320925651 startingPage "737" @default.
- W4320925651 abstract "There is an emotion related to everything a person does, and recognizing is a much complex task. These emotions perform very important role in understanding how a person feels about something. In this world, where humans are fed what others want through speech, then analyzing these different emotions is of major importance. In this paper, we will try to combine different dataset and formulate a base methodology. Different people have used techniques ranging from CNN, SVM to Fourier parameter. Language and geography also matter. Emotions and reaction pitches and energies are different for people of different culture, nation, and perspective. 1D CNN model is studied and concluded in present study. The different dataset is used by various researchers, and such dataset plays an important contribution in the results. Datasets which have voice with loudspeakers along with some background noise can decrease the accuracy drastically with our combined dataset we hope to minimize the dataset bad effect on the model and enable them to perform better on unseen data." @default.
- W4320925651 created "2023-02-16" @default.
- W4320925651 creator A5019120028 @default.
- W4320925651 creator A5032476929 @default.
- W4320925651 creator A5087030329 @default.
- W4320925651 date "2023-01-01" @default.
- W4320925651 modified "2023-09-28" @default.
- W4320925651 title "Exploring the Emotion Recognition in Speech Using Machine Learning" @default.
- W4320925651 cites W2030931454 @default.
- W4320925651 cites W2043152858 @default.
- W4320925651 cites W2123119128 @default.
- W4320925651 cites W2955291451 @default.
- W4320925651 cites W3000139089 @default.
- W4320925651 cites W3025283630 @default.
- W4320925651 cites W3089373398 @default.
- W4320925651 cites W3095648847 @default.
- W4320925651 cites W3120709499 @default.
- W4320925651 cites W3144479330 @default.
- W4320925651 cites W3162538529 @default.
- W4320925651 cites W3162811262 @default.
- W4320925651 cites W3163157357 @default.
- W4320925651 cites W3165803453 @default.
- W4320925651 cites W4205567678 @default.
- W4320925651 doi "https://doi.org/10.1007/978-981-19-7346-8_64" @default.
- W4320925651 hasPublicationYear "2023" @default.
- W4320925651 type Work @default.
- W4320925651 citedByCount "0" @default.
- W4320925651 crossrefType "book-chapter" @default.
- W4320925651 hasAuthorship W4320925651A5019120028 @default.
- W4320925651 hasAuthorship W4320925651A5032476929 @default.
- W4320925651 hasAuthorship W4320925651A5087030329 @default.
- W4320925651 hasConcept C115961682 @default.
- W4320925651 hasConcept C119599485 @default.
- W4320925651 hasConcept C119857082 @default.
- W4320925651 hasConcept C12267149 @default.
- W4320925651 hasConcept C12713177 @default.
- W4320925651 hasConcept C127413603 @default.
- W4320925651 hasConcept C154945302 @default.
- W4320925651 hasConcept C157138929 @default.
- W4320925651 hasConcept C201995342 @default.
- W4320925651 hasConcept C204321447 @default.
- W4320925651 hasConcept C2780451532 @default.
- W4320925651 hasConcept C28490314 @default.
- W4320925651 hasConcept C41008148 @default.
- W4320925651 hasConcept C99498987 @default.
- W4320925651 hasConceptScore W4320925651C115961682 @default.
- W4320925651 hasConceptScore W4320925651C119599485 @default.
- W4320925651 hasConceptScore W4320925651C119857082 @default.
- W4320925651 hasConceptScore W4320925651C12267149 @default.
- W4320925651 hasConceptScore W4320925651C12713177 @default.
- W4320925651 hasConceptScore W4320925651C127413603 @default.
- W4320925651 hasConceptScore W4320925651C154945302 @default.
- W4320925651 hasConceptScore W4320925651C157138929 @default.
- W4320925651 hasConceptScore W4320925651C201995342 @default.
- W4320925651 hasConceptScore W4320925651C204321447 @default.
- W4320925651 hasConceptScore W4320925651C2780451532 @default.
- W4320925651 hasConceptScore W4320925651C28490314 @default.
- W4320925651 hasConceptScore W4320925651C41008148 @default.
- W4320925651 hasConceptScore W4320925651C99498987 @default.
- W4320925651 hasLocation W43209256511 @default.
- W4320925651 hasOpenAccess W4320925651 @default.
- W4320925651 hasPrimaryLocation W43209256511 @default.
- W4320925651 hasRelatedWork W1996541855 @default.
- W4320925651 hasRelatedWork W2044113141 @default.
- W4320925651 hasRelatedWork W2180732726 @default.
- W4320925651 hasRelatedWork W2355927362 @default.
- W4320925651 hasRelatedWork W2937631562 @default.
- W4320925651 hasRelatedWork W2961085424 @default.
- W4320925651 hasRelatedWork W3195168932 @default.
- W4320925651 hasRelatedWork W4286629047 @default.
- W4320925651 hasRelatedWork W4306674287 @default.
- W4320925651 hasRelatedWork W4224009465 @default.
- W4320925651 isParatext "false" @default.
- W4320925651 isRetracted "false" @default.
- W4320925651 workType "book-chapter" @default.