## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Paper** & **Database** & & **Emotion** & **Features** & **Algorithm** & **Accuracy** \\ \hline \multirow{4}{*}{[PERSON] (2017)} & \multirow{4}{*}{(a)} & \multirow{4}{*}{EmoDB} & \multirow{4}{*}{(a)} & anger, excitement, & \multirow{4}{*}{} & \multirow{4}{*}{} & \multirow{4}{*}{} 3 Time-domain & \multirow{4}{*}{} & \multirow{4}{*}{} \\  & & & neutral, sad & & & & & \\  & & & natural, sad & & & & & \\  & & & & anger, Neutral, & & & & \\  & & & Happiness, Sadness, Fear & & & & & \\  & & & & & & & \\ \hline \multirow{2}{*}{Shaqr (2019)} & \multirow{2}{*}{RAVDESS} & \multirow{2}{*}{(a)} & \multirow{2}{*}{\begin{tabular}{c} anger, Boredom, Disgust, \\ Anxiety, Joy, Neutral, Sad \\ Disgust, Fear, Happy, \\ Neutral, Sad, Anger, \\ Calm \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} \(\bullet\) \\ 5 Spectral- \\ domain \\ 13 MFFCs \\ 13 Chroma: \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} LSTM with CTC loss \\ 71.08\% \\ \end{tabular} } & \multirow{2}{*}{(a)} & 54\% \\  & & & & & & \\ \hline \multirow{2}{*}{Shaqr (2019)} & \multirow{2}{*}{[PERSON]} & \multirow{2}{*}{\begin{tabular}{c} neutral, calm, happy, sad, angry, \\ fearful, disgust, surprised \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} \(\bullet\) \\ 56 eMAPS \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} LSTM with CTC loss \\ 71.08\% \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} (a) \\ 71.08\% \\ \end{tabular} } \\  & & & & & & \\ \hline \multirow{2}{*}{[PERSON] (2019)} & \multirow{2}{*}{RAVDESS} & \multirow{2}{*}{(a)} & \multirow{2}{*}{\begin{tabular}{c} Anger, Boredom, Disgust, \\ Anxiety, Joy, Neutral, Sad \\ Disgust, Fear, Happy, \\ Neutral, Sad, Anger, \\ Calm \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} MFCC \\ \end{tabular} } & \multirow{2}{*}{Logistic Model Tree} & \multirow{2}{*}{\begin{tabular}{c} (a) \\ (b) \\ \end{tabular} } & 64.51\% \\  & & & & & \\ \hline \multirow{2}{*}{[PERSON] (2016)} & \multirow{2}{*}{RECOLA} & \multirow{2}{*}{Arousal, Valence} & \multirow{2}{*}{-} & \multirow{2}{*}{1D Conv layer + LSTM} & \multirow{2}{*}{65.89\%} \\ \multirow{2}{*}{[PERSON] (2019)} & \multirow{2}{*}{(a)} & \multirow{2}{*}{\begin{tabular}{c} Anger, Boredom, Disgust, \\ Anxiety, Joy, Neutral, Sad \\ Anger, Excited, \\ Frustrated, Happy, \\ Neutral, Sad \\ \end{tabular} } & \multirow{2}{*}{Log mel-spectrogram} & \multirow{2}{*}{1D + 2D Conv layer + LSTM} & \multirow{2}{*}{\begin{tabular}{c} (a) \\ (b) \\ \end{tabular} } & 76.64\% \\  & & & & & \\ \hline \multirow{4}{*}{Proposed} & \multirow{4}{*}{(a)} & \multirow{4}{*}{EmoDB} & \multirow{4}{*}{\begin{tabular}{c} Anger, Neutral, \\ happiness, Sadness, Fear \\ neutral, calm, happy, sad, \\ angry, fearful, disgust, \\ surprised \\ \end{tabular} } & \multirow{4}{*}{\
\begin{tabular}{c} MFCC \\ \end{tabular} } & \multirow{4}{*}{(a)} & LSTM + Voting & \multirow{4}{*}{(a)} & \multirow{4}{*}{
\begin{tabular}{c} LSTM + Voting \\ \end{tabular} } \\  & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results from International DB. recognizing emotions after dividing speech into short intervals produces better recognition accuracy (83.81%) than recognizing speech emotion using one long LSTM model (75.51%). In future works, in order to accurately learn deep running-based models, a considerable amount of data is required. Studies into speech emotion recognition use far less data than studies based on videos or texts. It is necessary to build high-quality databases in order to improve performance and generalization. In order to recognize emotions from speech, it is necessary to find an accurate mapping of acoustic features and the intensity of emotions, using large databases. Although studies have used a variety of acoustic features, they still have the problem of not knowing what emotional intensity is manifested when a certain feature is found at a certain level.