# Emotion Recognition From Speech With Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1701.08071 · HTML: https://mlsanity.com/paper/1701.08071/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2017.

## Built on by (most cited first, 8 of 8)

- [Speech Emotion Diarization: Which Emotion Appears When?](https://mlsanity.com/paper/2306.12991/) (2023)
- [Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation](https://mlsanity.com/paper/2108.02510/) (2021)
- [Learning Speech Emotion Representations in the Quaternion Domain](https://mlsanity.com/paper/2204.02385/) (2022)
- [Designing and Evaluating Speech Emotion Recognition Systems: A reality check case study with IEMOCAP](https://mlsanity.com/paper/2304.00860/) (2023)
- [Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive Mixup](https://mlsanity.com/paper/2305.06273/) (2023)
- [Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction](https://mlsanity.com/paper/2204.11382/) (2022)
- [Training speech emotion classifier without categorical annotations](https://mlsanity.com/paper/2210.07642/) (2022)
- [Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis](https://mlsanity.com/paper/2503.21927/) (2025)

## Abstract (arXiv)

In this paper the task of emotion recognition from speech is considered. Proposed approach uses deep recurrent neural network trained on a sequence of acoustic features calculated over small speech intervals. At the same time special probabilistic-nature CTC loss function allows to consider long utterances containing both emotional and neutral parts. The effectiveness of such an approach is shown in two ways. Firstly, the comparison with recent advances in this field is carried out. Secondly, human performance on the same task is measured. Both criteria show the high quality of the proposed method.