# Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles

arXiv: https://arxiv.org/abs/2510.03060 · HTML: https://mlsanity.com/paper/2510.03060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Multimodal Speech Emotion Recognition Using Audio and Text](https://mlsanity.com/paper/1810.04635/) (2018)
- [Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrum](https://mlsanity.com/paper/2404.17862/) (2024)
- [LSSED: a large-scale dataset and benchmark for speech emotion recognition](https://mlsanity.com/paper/2102.01754/) (2021)
- [Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generation](https://mlsanity.com/paper/2301.04907/) (2023)

## Abstract (arXiv)

Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In this study, we distinguish between descriptive semantics, which represents the contextual content of speech, and expressive semantics, which reflects the speaker's emotional state. After watching emotionally charged movie segments, we recorded audio clips of participants describing their experiences, along with the intended emotion tags for each clip, participants' self-rated emotional responses, and their valence/arousal scores. Through experiments, we show that descriptive semantics align with intended emotions, while expressive semantics correlate with evoked emotions. Our findings inform SER applications in human-AI interaction and pave the way for more context-aware AI systems.