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

• URL: http://arxiv.org/abs/2307.11584v1
• Date: Fri, 21 Jul 2023 13:48:11 GMT
• Title: A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion
• Authors: Zeinab Sadat Taghavi, Ali Satvaty, Hossein Sameti
• Abstract summary: 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. 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.
• Score: 0.6767885381740951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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