# EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2310.12851 · HTML: https://mlsanity.com/paper/2310.12851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 19 Oct 2023.

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

- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers](https://mlsanity.com/paper/2203.17068/) (2022)
- [Human-Centered Emotion Recognition in Animated GIFs](https://mlsanity.com/paper/1904.12201/) (2019)
- [An Experimental Review of Speaker Diarization methods with application to Two-Speaker Conversational Telephone Speech recordings](https://mlsanity.com/paper/2305.18074/) (2023)
- [Semi-supervised multi-channel speaker diarization with cross-channel attention](https://mlsanity.com/paper/2307.08688/) (2023)

## Built on by (most cited first, 1 of 1)

- [Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis](https://mlsanity.com/paper/2503.21927/) (2025)

## Abstract (arXiv)

In the era of advanced artificial intelligence and human-computer interaction, identifying emotions in spoken language is paramount. This research explores the integration of deep learning techniques in speech emotion recognition, offering a comprehensive solution to the challenges associated with speaker diarization and emotion identification. It introduces a framework that combines a pre-existing speaker diarization pipeline and an emotion identification model built on a Convolutional Neural Network (CNN) to achieve higher precision. The proposed model was trained on data from five speech emotion datasets, namely, RAVDESS, CREMA-D, SAVEE, TESS, and Movie Clips, out of which the latter is a speech emotion dataset created specifically for this research. The features extracted from each sample include Mel Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), Root Mean Square (RMS), and various data augmentation algorithms like pitch, noise, stretch, and shift. This feature extraction approach aims to enhance prediction accuracy while reducing computational complexity. The proposed model yields an unweighted accuracy of 63%, demonstrating remarkable efficiency in accurately identifying emotional states within speech signals.