  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.
