# Emotion Recognition from Speech in a Subject-Independent Approach
## 6 Results and Discussion
### Comparison with Other Results  
The literature shows significant variability in the results obtained for emotion recognition tasks, which stems from differences in the datasets used as well as the applied training methods and classification strategies. Various datasets differ in terms of the range of emotions covered, the quality and duration of recordings, the number of speakers, and the recording conditions, all of which affect the task's complexity and the performance metrics achieved. Additionally, the choice of data splitting techniques (e.g., random splitting or subject-independent splitting), feature extraction methods, model architectures, and validation strategies can have a substantial impact on the final results. In [75], a comprehensive speech emotion recognition framework combining ZCR, RMS, and MFCC feature sets was introduced. The approach utilizes both CNN and LSTM architectures, enhanced by an attention mechanism. The model was evaluated on two datasets: the TESS and RAVDESS. Results showed outstanding performance, achieving accuracy rates of 0.998 on TESS and 0.957 on the RAVDESS. In [76], the researchers introduced an approach to speech emotion recognition that integrates mel spectrograms with short-term Fourier transforms and enhanced multiscale vision transformers (IMVTs). Their experiments showed that this technique achieves strong generalization across different datasets, reporting accuracies of 0.915 for Emo-DB, 0.818 for the RAVDESS, and 0.640 for IEMOCAP. The work in [77] presented a method aimed at improving the recognition of emotions in speech through feature enhancement. The authors utilized the INTERSPEECH 2010 feature set, selected specific feature subsets, and then applied principal component analysis. As a result, their method outperformed the baseline with average recognition improvements of 0.115 for six out of seven emotions in EMO-DB and 0.138 for seven out of eight emotions in the RAVDESS. Reference [78] proposed the use of a one-dimensional dilated convolutional neural network (1D-DCNN) combined with hierarchical feature learning blocks and a bi-directional gated recurrent unit (BiGRU) for emotion recognition from speech. This architecture was tested on IEMOCAP, EMO-DB, and RAVDESS datasets, yielding accuracies of 0.728, 0.911, and 0.780, respectively. In [79], a neural network-based system was designed for detecting depression using audio features extracted from spectrograms, enabling the differentiation between speech patterns of depressed and non-depressed individuals. Their multimodal solution merged mel-frequency cepstral coefficients with features obtained from spectrograms using a CNN, achieving a detection accuracy above 0.850 on the RAVDESS. In [75], a speech emotion recognition method based on Variational Mode Decomposition (VMD) and adaptive mode selection using energy information was presented. The framework was evaluated on publicly available acted and elicited datasets. For the acted datasets, it achieved accuracy rates of 0.938 on the RAVDESS-speech, 0.958 on Emo-DB, and 0.934 on EMOVO, each representing different languages. The study in [80] evaluated the effectiveness of combining mel-frequency cepstral coefficients, spectrograms, and mel-spectrograms as one-dimensional input vectors for both CNN and DNN classifiers in speech emotion recognition. The fusion of these features led to better results than when using them individually, with DNN classifiers achieving accuracies of 0.766, 0.871, 0.798, and 1.00, and CNN classifiers obtaining 0.750, 0.841, 0.781, and 1.00 for the RAVDESS, EMO-DB, and SAVEE datasets, respectively. In [81], the authors introduce a multi-feature learning network with dynamic-static feature fusion (ML-DSF) to enhance feature representation for SER. The approach combines a self-calibration module for static feature extraction from log-mel spectrograms and a lightweight temporal convolutional network for dynamic features from MFCCs. These are integrated using an attention module optimized by PCA.