ArticleSensors (Basel, Switzerland)2020
Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Stacked convolutional neural network for emotion recognition using multi feature speech analysis.Scientific reports · 2025Article
- Speech emotion classification using attention based network and regularized feature selection.Scientific reports · 2023Article
- Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer.Sensors (Basel, Switzerland) · 2023Article
- IoT-Enabled WBAN and Machine Learning for Speech Emotion Recognition in Patients.Sensors (Basel, Switzerland) · 2023Article
- A Bimodal Emotion Recognition Approach through the Fusion of Electroencephalography and Facial Sequences.Diagnostics (Basel, Switzerland) · 2023Article
- Human-Computer Interaction with a Real-Time Speech Emotion Recognition with Ensembling Techniques 1D Convolution Neural Network and Attention.Sensors (Basel, Switzerland) · 2023Article
- Convolutional neural network in rice disease recognition: accuracy, speed and lightweight.Frontiers in plant science · 2023Review
- M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.Sensors (Basel, Switzerland) · 2022Article
- Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification.Sensors (Basel, Switzerland) · 2022Article
- Automated identification of chicken distress vocalizations using deep learning models.Journal of the Royal Society, Interface · 2022Article
- Speaker Recognition Using Constrained Convolutional Neural Networks in Emotional Speech.Entropy (Basel, Switzerland) · 2022Article
- Speech emotion recognition based on improved masking EMD and convolutional recurrent neural network.Frontiers in psychology · 2022Article
- Establishment and psychometric characteristics of emotional words list for suicidal risk assessment in speech emotion recognition.Frontiers in psychiatry · 2022Article
- Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- The Impact of Attention Mechanisms on Speech Emotion Recognition.Sensors (Basel, Switzerland) · 2021Article
- Age and Gender Recognition Using a Convolutional Neural Network with a Specially Designed Multi-Attention Module through Speech Spectrograms.Sensors (Basel, Switzerland) · 2021Article
- Cascaded Convolutional Neural Network Architecture for Speech Emotion Recognition in Noisy Conditions.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Data, Signal and Image Processing and Applications in Sensors.Sensors (Basel, Switzerland) · 2021Article
- Context-Aware Emotion Recognition in the Wild Using Spatio-Temporal and Temporal-Pyramid Models.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition is a challenging task for a machine. In addition, making it smarter so that the emotions are efficiently recognized by AI is equally challenging. The speech signal is quite hard to examine using signal processing methods because it consists of different frequencies and features that vary according to emotions, such as anger, fear, sadness, happiness, boredom, disgust, and surprise. Even though different algorithms are being developed for the SER, the success rates are very low according to the languages, the emotions, and the databases. In this paper, we propose a new lightweight effective SER model that has a low computational complexity and a high recognition accuracy. The suggested method uses the convolutional neural network (CNN) approach to learn the deep frequency features by using a plain rectangular filter with a modified pooling strategy that have more discriminative power for the SER. The proposed CNN model was trained on the extracted frequency features from the speech data and was then tested to predict the emotions. The proposed SER model was evaluated over two benchmarks, which included the interactive emotional dyadic motion capture (IEMOCAP) and the berlin emotional speech database (EMO-DB) speech datasets, and it obtained 77.01% and 92.02% recognition results. The experimental results demonstrated that the proposed CNN-based SER system can achieve a better recognition performance than the state-of-the-art SER systems.
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