## 2 Related Works
### Speech Emotion Recognition  
Speech signals are some of the most natural media of human communication, and they have the merit of real-time simple measurement. They contain implicit paralinguistic information and linguistic content, including the speaker's emotions. In the designing of a speech emotion recognition system, it is a challenging task to identify and extract different emotion related speech features. Since proper selection of the features affects the classification performance, it is critical to combine appropriate audio features in speech emotion recognition. There were many approaches to recognizing emotion from speech, and each study used different speech features. Linear Prediction Cepstrum Coefficients, MFCCs, and F0 have been widely used for the recognition of speech emotion. However, the question of whether these features are effective for classifying emotions is still under discussion in many studies [23]. A few studies extracted features from speech and recognized emotions utilizing Gaussian Mixture Models (GMMs) [24] and Hidden Markov Models (HMMs) [25]. Recently, with the rapid development of deep-learning algorithms, RNN have been applied to various fields of speech analysis [26; 27; 28]. The main focus of the approach [26] is to classify the emotions in an utterance, rather than classifying the emotions using the frames of the utterance. To extract global features, the authors fed 32-dimensional frame features directly into an RNN. Then, the global features were fed into an extreme learning machine to classify the emotions. This approach recognizes emotions from an utterance, rather than using the frames of the utterance, and therefore requires a lot of computational power to train the network. In another approach, [PERSON] et al. [29] found that binary decision trees can determine the features most relevant to emotions. However, their experiment was performed using a small dataset. A study [30] assumed that the characteristics of speech vary in each person, and that emotions are affected by the age, gender, and acoustic features of the speaker. The authors focused on speech emotion recognition by grouping speech data by age and gender. They proposed a hierarchical gender- and age-based model and utilized different feature vectors of OpenSmile [31] and eGeMAPS [32]. The results indicated that building a separate classifier for each gender and age group produces better performance than having one model for all genders and ages. [PERSON] and [PERSON] extracted acoustic features by classifying the features into three categories [33]:  
* 3 Time domain: zero crossing rate, energy, and entropy of energy
* 5 Spectral domain: spectral centroid, spectral spread, spectral entropy, spectral flux, and spectral roll-off
* 13 MFCCs
* 13 Chroma: 12-dimensional chroma vector, standard deviation of chroma vector  
The total set of acoustic features is a sequence of 34-dimensional vectors for each utterance.  
An RNN with a Connectionist Temporal Classification approach [34] was used to classify emotions from speech. [PERSON] et al. proposed two CNN LSTM networks created by stacking local feature learning blocks and other layers, to extract emotional features [9]. The 1D CNN LSTM network was intended to recognize speech emotion and extract deep features from a raw signal. The 2D CNN LSTM network focused mainly on learning global contextual information from a log-mel spectrogram. The CNN LSTM networks could recognize speech emotion effectively, without using hand-crafted features.  
Many studies have demonstrated the correlation between emotional voices and acoustic features [35; 36; 37; 38; 39; 40]. However, because there does not exist overt and deterministic mapping between features and emotional state, speech emotion recognition still has lower recognition rate than other emotion-recognition methods, such as FER. For this reason, finding the appropriate feature combination is a critical task in speech-based emotion recognition.