## 2 Related Works  
One of the most critical factors to consider when building an SER system is finding the most effective speech features to discriminate emotions from a speech signal. To solve this challenge, many researchers have investigated a massive number of speech features and achieved considerably good results in the arousal dimension (excited versus calm). For instance, CEICES systematically analyzed the acoustic features of speech to find the best acoustic feature set. They combined the acoustic features which they had and chose the best feature set based on classification accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Vocal Expression**} \\ \hline
**Acoustic Features** & **Anger** & **Fear** & **Happiness** & **Sadness** & **Tenderness** \\ \hline speech rate & fast & fast & fast & slow & slow \\ voice intensity & high & high & high & low & low \\ voice intensity variability & high & high & high & low & low \\ high-frequency energy & high & high & high & low & low \\ F0 & high & high & high & low & low \\ F0 variability & high & low & high & low & low \\ F0 contours & up & up & up & down & down \\ voice onsets & fast & fast & fast & slow & slow \\ microstructural regularity & irregular & irregular & regular & irregular & regular \\ \hline \hline  & \multicolumn{4}{c}{**Music Performance**} \\ \hline tempo & fast & fast & fast & slow & slow \\ sound level & high & low & medium & low & low \\ sound level variability & high & high & low & low & low \\ high-frequency energy & high & low & medium & low & low \\ F0 & sharp & sharp & sharp & flat & low \\ F0 variability & high & low & high & low & low \\ pitch contours & up & up & up & down & down \\ tone attack & fast & slow & fast & slow & slow \\ microstructural regularity & irregular & irregular & regular & irregular & regular \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acoustic feature characteristics of discrete emotions expressed in speech and music performances. In recent studies, the investigation of the acoustic features not only in the arousal dimension (excited versus calm), but also in the valence dimension (positive versus negative) has increased. [PERSON] and [PERSON] analyzed duration, F0, voice quality, and intensity features of an emotional speech signal to determine the role of those acoustic features regarding the arousal, valence, and potency/control emotional dimensions [21]. Their study showed that the variation of intensity of positive emotions was less than the variation of intensity of negative emotions. Moreover, positive emotions have a steeper spectral slope compared to negative emotions. Finally, they concluded that spectral shape features, and the speaking rate, which is related to rhythm, are the critical acoustic features for discriminating emotions in the valence dimension. [PERSON] et al. [22] reported the importance of cepstral features (Mel frequency cepstral coefficients--MFCC). MFCC is closely related to spectral shape features. Speech features, such as energy, F0, voice quality, spectral, MFCC, and RASTA style-filtered auditory spectrum features of speech were analyzed to determine the relative effectiveness of these acoustic features in the valence dimension in [8]. They concluded that MFCC and RASTA style-filtered auditory spectra were the most relevant acoustic features for the valence dimension. Furthermore, the vital role of spectral shape and slope was studied and confirmed by [23; 24]. Recently, [PERSON] et al. proposed the GeMAPS for voice research [5]. They chose acoustic features based on physiological changes in voice production, automatic extractability, and theoretical significance. This acoustic feature set included frequency-related (pitch, jitter), energy/amplitude related (shimmer, loudness), spectral (alpha ratio, spectral slope), and temporal (mean length, a rate of loudness peaks) features.