# A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering

arXiv: https://arxiv.org/abs/2507.07046 · HTML: https://mlsanity.com/paper/2507.07046/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 49 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Yara Parser: A Fast and Accurate Dependency Parser](https://mlsanity.com/paper/1503.06733/) (2015)
- [An Ensemble 1D-CNN-LSTM-GRU Model with Data Augmentation for Speech Emotion Recognition](https://mlsanity.com/paper/2112.05666/) (2021)
- [A cross-corpus study on speech emotion recognition](https://mlsanity.com/paper/2207.02104/) (2022)

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

- [Spectrogram features for audio and speech analysis](https://mlsanity.com/paper/2603.14917/) (2026)

## Abstract (arXiv)

Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artificial intelligence (AI). Our proposed DCRF-BiLSTM model is used to recognize seven emotions: neutral, happy, sad, angry, fear, disgust, and surprise, which are trained on five datasets: RAVDESS (R), TESS (T), SAVEE (S), EmoDB (E), and Crema-D (C). The model achieves high accuracy on individual datasets, including 97.83% on RAVDESS, 97.02% on SAVEE, 95.10% for CREMA-D, and a perfect 100% on both TESS and EMO-DB. For the combined (R+T+S) datasets, it achieves 98.82% accuracy, outperforming previously reported results. To our knowledge, no existing study has evaluated a single SER model across all five benchmark datasets (i.e., R+T+S+C+E) simultaneously. In our work, we introduce this comprehensive combination and achieve a remarkable overall accuracy of 93.76%. These results confirm the robustness and generalizability of our DCRF-BiLSTM framework across diverse datasets.