ArticleSensors (Basel, Switzerland)2022
Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of traditional and deep learning methods for detecting depression based on speech features: a systematic review and meta-analysis.BMC psychiatry · 2025Pooled it
- Speech emotion recognition based on a stacked autoencoders optimized by PSO based grass fibrous root optimization.Scientific reports · 2025Article
- MS-EmoBoost: a novel strategy for enhancing self-supervised speech emotion representations.Scientific reports · 2025Article
- Fusion of PCA and ICA in Statistical Subset Analysis for Speech Emotion Recognition.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Speech Emotion Recognition Using Dual Feature Extraction Encoders.Sensors (Basel, Switzerland) · 2023Article
- Speech Emotion Recognition Using Attention Model.International journal of environmental research and public health · 2023Article
- Emotion Detection Based on Pupil Variation.Healthcare (Basel, Switzerland) · 2023Article
- Improved Feature Parameter Extraction from Speech Signals Using Machine Learning Algorithm.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.