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.
In the case of the different representations included in the experiments (Wav2vec 2.0 and eGeMAPS), a baseline result has been computed
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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.