# Emotion Recognition from Speech in a Subject-Independent Approach
## 5 Methods
### Applied Classification Methods  
The support vector machine with a cubic kernel (cubic SVM) is an effective tool for classifying emotions based on the analysis of vocal features, especially when utilizing many different features [65; 66]. The choice of this method is based on its adaptive properties and the ability to handle high-dimensional data. The cubic SVM is valued for its ability to efficiently classify data that are not linearly separable in the input space. By transforming \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Description** & **Number of Features** \\ \hline Energy features & RMS value (RMS), mean value of energy (Eavg), maximum value of energy (Emax), minimum value of energy (Emin), energy variance (Evar), mean value of RMS value (RMSavg), maximum of RMS value (RMSmax), minimum of RMS value (RMSmin), variance of RMS value (RMSvar), mean logarithm of energy (IEvar), maximum value of logarithm of energy (IEmax), minimum value of logarithm of energy (IEmin) & 13 \\ \hline Mel-cepstral features & Dcavg (Dcavg), variance of delta coefficient (Dcvar), maximum value of delta coefficient (Dcmin), mean value of delta-delta coefficient (Ddcvar), minimum value of delta-delta coefficient (Ddcmax), mean value of the i-th mel-cepstral coefficient (Mc(i)vg), variance value of the i-th mel-cepstral coefficient (Mc(i)jmax), maximum value of the i-th mel-cepstral coefficient (Mc(i)jmax), minimum value of the i-th mel-cepstral coefficient (Mc(i)jmin) & 13 \\ \hline Zero crossing density & Mean value of zero crossings density (GP0 avg), variance of zero crossings density (GP0 var), maximum value of zero crossings density (GP0 max), minimum value of zero crossings density (GP0 min) & 14 \\ \hline Fundamental frequency & Fundamental frequency (F0 avg), variance of the fundamental frequency (F0 var), maximum value of the fundamental frequency (F0 max), minimum value of the fundamental frequency (F0 min) & 15 \\ \hline Other spectral features & Mean value of the center of gravity of the spectrum (SCWavg), variance of the center of gravity of spectrum (SCWvar), maximum value of the center of gravity of spectrum (SCWmax), the mean value of spectral flux (SFavg), variance of spectral flux (SFvar), mean value of spectral roll of r p threshold (SRO(p)avg), variance of spectral roll off for p threshold (SRO(p)var), maximum value of spectral roll off for p threshold (SRO(p)max), minimum value of spectral roll off for p threshold (SRO(p)min) & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of the calculated features. data into a higher-dimensional space, the cubic SVM enables finding a hyperplane that best separates the data classes. The cubic kernel further enhances the model's ability to deal with the complexity and nonlinearity of vocal features, resulting in higher classification accuracy. Long short-term memory (LSTM) is a highly effective method for classifying vocal features to distinguish emotions, due to its properties of sequential data processing and the ability to capture temporal dependencies in the data [67,68]. The LSTM network used in the studies begins with an input layer. The network then utilizes two bidirectional LSTM layers, each with 150 units. The first of these layers returns a sequence output, maintaining sequential information at every time step, which is vital for processing information with context in both directions of the sequence. The second bidirectional LSTM layer is configured to return only the last output value, useful for summarizing information from the entire sequence into a single vector. In the next stage, the network uses a fully connected layer, with the number of neurons equal to the number of labels in the training set. This layer aims to map the accumulated features to the appropriate classes.