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• W2919787052 abstract "A common step in the area of speech emotion recognition is to obtain ground-truth labels describing the emotional content of a sentence. The underlying emotion of a given recording is usually unknown, so perceptual evaluations are conducted to annotate its perceived emotion. Each sentence is often annotated by multiple raters, which are aggregated with methods such as majority vote rules. This paper argues that several labels provided by different individuals convey more information than the consensus labels. We demonstrate that leveraging the information provided by separate evaluations collected by multiple raters can help in building more robust classifiers which maximize the utilization of labeled data. Motivated by the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>synthetic minority over-sampling technique</i> (SMOTE), we present a novel over-sampling approach during training, where the samples with categorical emotion labels are over-sampled according to the labels assigned by multiple individuals. This approach (1) increases the number of sentences from classes with underrepresented consensus labels, and (2) utilizes sentences with ambiguous emotional content even if they do not reach consensus agreement. The experimental evaluation shows the benefits of the approach over a baseline classifier trained with consensus labels, which increases the F1-score by 5.2 percent (absolute) for the USC-IEMOCAP corpus, and 5.4 percent (absolute) for the MSP-IMPROV corpus." @default.
• W2919787052 created "2019-03-11" @default.
• W2919787052 creator A5040793194 @default.
• W2919787052 creator A5041789037 @default.
• W2919787052 date "2021-10-01" @default.
• W2919787052 modified "2023-09-23" @default.
• W2919787052 title "Over-Sampling Emotional Speech Data Based on Subjective Evaluations Provided by Multiple Individuals" @default.
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• W2919787052 doi "https://doi.org/10.1109/taffc.2019.2901465" @default.
• W2919787052 hasPublicationYear "2021" @default.
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