# Detecting Emotion Carriers by Combining Acoustic and Lexical Representations

arXiv: https://arxiv.org/abs/2112.06603 · HTML: https://mlsanity.com/paper/2112.06603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Open Source Automatic Speech Recognition for German](https://mlsanity.com/paper/1807.10311/) (2018)
- [Annotation of Emotion Carriers in Personal Narratives](https://mlsanity.com/paper/2002.12196/) (2020)
- [Modeling user context for valence prediction from narratives](https://mlsanity.com/paper/1905.05701/) (2019)
- [Emotion Carrier Recognition from Personal Narratives](https://mlsanity.com/paper/2008.07481/) (2020)

## Abstract (arXiv)

Personal narratives (PN) - spoken or written - are recollections of facts, people, events, and thoughts from one's own experience. Emotion recognition and sentiment analysis tasks are usually defined at the utterance or document level. However, in this work, we focus on Emotion Carriers (EC) defined as the segments (speech or text) that best explain the emotional state of the narrator ("loss of father", "made me choose"). Once extracted, such EC can provide a richer representation of the user state to improve natural language understanding and dialogue modeling. In previous work, it has been shown that EC can be identified using lexical features. However, spoken narratives should provide a richer description of the context and the users' emotional state. In this paper, we leverage word-based acoustic and textual embeddings as well as early and late fusion techniques for the detection of ECs in spoken narratives. For the acoustic word-level representations, we use Residual Neural Networks (ResNet) pretrained on separate speech emotion corpora and fine-tuned to detect EC. Experiments with different fusion and system combination strategies show that late fusion leads to significant improvements for this task.