Using GPT-2 surprisal to select a few words per sentence for acoustic feature extraction gives a small accuracy improvement over whole-utterance features in RAVDESS speech emotion recognition, though the corpus's two fixed sentences limit the finding.
This in- cludes an analysis of word surprisal, word rank, segment chunk- ing, and the setup for supervised classification
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Investigating the Impact of Word Informativeness on Speech Emotion Recognition
Using GPT-2 surprisal to select a few words per sentence for acoustic feature extraction gives a small accuracy improvement over whole-utterance features in RAVDESS speech emotion recognition, though the corpus's two fixed sentences limit the finding.