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A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion

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arxiv 2307.11584 v1 pith:UEPANL7I submitted 2023-07-21 cs.SD cs.CLcs.LGeess.AS

Zeinab Sadat Taghavi , Ali Satvaty , Hossein Sameti This is my paper · ORCID
classification cs.SDcs.CLcs.LGeess.AS
keywords conversionmethodmodalityrecognitionemotionspeechdatasetfirst
verification ladder T0 review T1 audit T2 compute T3 formal
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Speech Emotion Recognition (SER) is a challenging task. In this paper, we introduce a modality conversion concept aimed at enhancing emotion recognition performance on the MELD dataset. We assess our approach through two experiments: first, a method named Modality-Conversion that employs automatic speech recognition (ASR) systems, followed by a text classifier; second, we assume perfect ASR output and investigate the impact of modality conversion on SER, this method is called Modality-Conversion++. Our findings indicate that the first method yields substantial results, while the second method outperforms state-of-the-art (SOTA) speech-based approaches in terms of SER weighted-F1 (WF1) score on the MELD dataset. This research highlights the potential of modality conversion for tasks that can be conducted in alternative modalities.

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Cited by 3 Pith papers

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