# CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation

arXiv: https://arxiv.org/abs/1802.05630 · HTML: https://mlsanity.com/paper/1802.05630/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 17 of 17)

- [Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition](https://mlsanity.com/paper/2110.06309/) (2021)
- [Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation](https://mlsanity.com/paper/2108.02510/) (2021)
- [Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models](https://mlsanity.com/paper/2202.08974/) (2022)
- [Emotion Recognition In Persian Speech Using Deep Neural Networks](https://mlsanity.com/paper/2204.13601/) (2022)
- [Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer](https://mlsanity.com/paper/2211.08843/) (2022)
- [Learning Multilingual Expressive Speech Representation for Prosody Prediction without Parallel Data](https://mlsanity.com/paper/2306.17199/) (2023)
- [MSP-Podcast SER Challenge 2024: L'antenne du Ventoux Multimodal Self-Supervised Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2407.05746/) (2024)
- [Enhancing Generalization in PPG-Based Emotion Measurement with a CNN-TCN-LSTM Model](https://mlsanity.com/paper/2507.14173/) (2025)
- [A novel policy for pre-trained Deep Reinforcement Learning for Speech Emotion Recognition](https://mlsanity.com/paper/2101.00738/) (2021)
- [Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search](https://mlsanity.com/paper/2211.08237/) (2022)
- [Enhancing Speech Emotion Recognition Through Differentiable Architecture Search](https://mlsanity.com/paper/2305.14402/) (2023)
- [Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method](https://mlsanity.com/paper/2111.07094/) (2021)
- [Representation Learning with Parameterised Quantum Circuits for Advancing Speech Emotion Recognition](https://mlsanity.com/paper/2501.12050/) (2025)
- [EmoAugNet: A Signal-Augmented Hybrid CNN-LSTM Framework for Speech Emotion Recognition](https://mlsanity.com/paper/2508.06321/) (2025)
- [Speech Emotion Recognition Using MFCC Features and LSTM-Based Deep Learning Model](https://mlsanity.com/paper/2604.25938/) (2026)
- [Low-resource Language Discrimination Towards Chinese Dialects with Transfer learning and Data Augmentation](https://mlsanity.com/paper/2606.18597/) (2026)
- [Explainable Lightweight Compact Deep Models for Speech Emotion Recognition](https://mlsanity.com/paper/2607.16803/) (2026)

## Abstract (arXiv)

In this work we design a neural network for recognizing emotions in speech, using the IEMOCAP dataset. Following the latest advances in audio analysis, we use an architecture involving both convolutional layers, for extracting high-level features from raw spectrograms, and recurrent ones for aggregating long-term dependencies. We examine the techniques of data augmentation with vocal track length perturbation, layer-wise optimizer adjustment, batch normalization of recurrent layers and obtain highly competitive results of 64.5% for weighted accuracy and 61.7% for unweighted accuracy on four emotions.