# Speech Emotion Recognition through Hybrid Features and Convolutional Neural Network
## 2 Related Work
### Feature Extraction  
Two approaches are widely used in the literature to derive features from audio signals. The first approach involves dividing the signal into voice frames of a specific duration and extract low-level features from each single frame. The features used for SER are generally categorized into four categories: linguistic, contextual, acoustic, and hybrid. Acoustic features are the most popular and effective features used in SER. These comprise voice quality features (jitter, shimmer, first three formants, and harmonic to noise ratio), prosodic features (pitch, loudness, and duration), as well as spectral features (MFCCs) [9]. Ref. [16] extracted 6 distinct acoustic features, namely energy, amplitude energy, log energy, formants, pitch, 10-order Linear Predication Cepstral Coefficients (LPCCs), and 12-order MFCCs from each frame. Afterwards, the Hidden Markov Model (HMM) was used to identify emotions, which obtained an accuracy of 76.1% on the Beihang University Mandarin dataset. Recently, the use of Deep Learning (DL) architectures for SER has gained interest due to the rapid development in computational technology and challenges faced by traditional ML algorithms in handling large datasets. For this, [PERSON] and [PERSON] [17] provided a comprehensive review on SER and highlighted three effective approaches to identify emotions. The review further reported the importance of employing optimized classification algorithms to enhance the robustness of SER system. In another study, [PERSON] et al. [9] reviewed DL techniques with characteristics, pros, and cons. This study also classified DL techniques into discriminative (Recurrent Neural Network, CNN), generative (Deep Belief Network, restricted Boltzmann machine, and deep autoencoders), and hybrid techniques. Additionally, the authors investigated the optimization of DL methods for SER. [PERSON]. [18] proposed a method to derive multimodal feature representations using multiple CNNs. The reported method derived suitable features, including two-dimensional log Mel-spectrograms, and three-dimensional temporal dynamics from voice. The multiple CNNs (1D, 2D, and 3D) were trained separately, and finally a fusion based on scores fusion was performed to identify emotions. The study achieved a weighted accuracy of 35.7% on AFEW5.0, and 44.1% on BAUM-1 datasets. In another study [19] a DL technique was proposed for emotion prediction from audio files. The technique involved preprocessing raw signals to derive features such as energy, pitch, and MFCCs followed by the selection of relevant features using a feature selection method. A CNN model was trained for classification. The proposed technique achieved an accuracy of 93.8% on the Emo-DB dataset, outperforming the baseline k-NN classifier's accuracy of 92.3%. [PERSON]. [20] presented a novel SER framework that employed ID CNN and combined five features (Chromagram, MFCCs, Mel-Spectrogram, Tonnetz, and Contrast) as the input. The model was evaluated on IEMOCAP, RAVDESS, and EMO-DB datasets and achieved good accuracy. To improve the accuracy and reduce computational cost and processing time, [PERSON]. [21] proposed a novel SER method. The study obtained a useful sequence from signals using the K-means clustering algorithm and generated spectrograms using the Short-time Fourier transform (STFT) method. A ResNet CNN model was then utilized to extract relevant features from spectrograms, which were given to the BiLSTM model to predict the emotions. The pro posed technique achieved 77.0% accuracy for the RAVDESS dataset, 72.3% for IEMOCAP, and 85.6% for EMO-DB. [PERSON], et al. [22] reported a CNN-based model to automatically derive features from audio files and identify emotions. The authors generated spectrograms with a 50% overlap from signals and trained two CNNs with different kernel sizes and pooling techniques on resized spectrograms. The results indicated that rectangular kernel and max-pool operations are the most efficient techniques for SER. [PERSON].