# Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis

arXiv: https://arxiv.org/abs/2503.21927 · HTML: https://mlsanity.com/paper/2503.21927/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 7 of 7)

- [Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances](https://mlsanity.com/paper/1905.02947/) (2019)
- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognition](https://mlsanity.com/paper/2211.11256/) (2022)
- [EmoBench: Evaluating the Emotional Intelligence of Large Language Models](https://mlsanity.com/paper/2402.12071/) (2024)
- [Emotion Recognition From Speech With Recurrent Neural Networks](https://mlsanity.com/paper/1701.08071/) (2017)
- [EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks](https://mlsanity.com/paper/2310.12851/) (2023)
- [Generative Technology for Human Emotion Recognition: A Scope Review](https://mlsanity.com/paper/2407.03640/) (2024)

## Abstract (arXiv)

This research presents a hybrid emotion recognition system integrating advanced Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLMs) to analyze audio and textual data for enhancing customer interactions in contact centers. By combining acoustic features with textual sentiment analysis, the system achieves nuanced emotion detection, addressing the limitations of traditional approaches in understanding complex emotional states. Leveraging LSTM and CNN models for audio analysis and DistilBERT for textual evaluation, the methodology accommodates linguistic and cultural variations while ensuring real-time processing. Rigorous testing on diverse datasets demonstrates the system's robustness and accuracy, highlighting its potential to transform customer service by enabling personalized, empathetic interactions and improving operational efficiency. This research establishes a foundation for more intelligent and human-centric digital communication, redefining customer service standards.