###### Abstract  
The goal of the human interface is to recognize the user's emotional state precisely. In the speech emotion recognition study, the most important issue is the effective parallel use of the extraction of proper speech features and an appropriate classification engine. Well defined speech databases are also needed to accurately recognize and analyze emotions from speech signals. In this work, we constructed a Korean emotional speech database for speech emotion analysis and proposed a feature combination that can improve emotion recognition performance using a recurrent neural network model. To investigate the acoustic features, which can reflect distinct momentary changes in emotional expression, we extracted F0, Mel-frequency cepstrum coefficients, spectral features, harmonic features, and others. Statistical analysis was performed to select an optimal combination of acoustic features that affect the emotion from speech. We used a recurrent neural network model to classify emotions from speech. The results show the proposed system has more accurate performance than previous studies.  
speech emotion recognition; recurrent neural network; speech analysis +
Footnote †: journal: Medical  
emotion recognition (FER). Therefore, it is critical to combine appropriate audio features in speech-emotion recognition.  
To accurately recognize and analyze emotions from speech, it is also important to construct a superior speech database. The key to these studies is to guarantee verified, reliable expressions of emotion. Therefore, statistical evaluation of whether speech data involves emotions is needed. To meet these needs, many studies have tried to construct speech databases, and a growing number of speech emotion databases have therefore become available [19; 20; 21]. However, there are no public Korean speech emotion databases. Since there are differences in the expression of emotions in speech which depend on the culture and language of the country, a Korean-based emotional speech database is essential to the analysis of emotions based on Korean speech. In this work, we constructed a Korean emotional speech database (K-EmoDB) for speech emotion analysis and proposed a feature combination that can improve emotion recognition performance, using a Recurrent Neural Network (RNN) model [22]. To do this, the database was recorded using two different methods. The emotion categories involved were (\"Neutrality\", \"Happiness\", \"Anger\", \"Sadness\", \"Excitement\", and \"Fear\"). All data were recorded in a professional studio, to maximize the sound quality by eliminating any background noise. To ensure the emotional quality and naturalness of the data, an evaluation test was carried out with seven subjects. The subjects listened to each sample and had to decide on an emotion score from 5 to 1. Based on the evaluation results, 150 emotion data points in each category were chosen to construct the final Korean emotional speech database. To investigate the acoustic features, which can reflect distinct momentary changes in emotional expression, we extracted fundamental frequency (F0), MFCC, chroma, spectral features, harmonic features, and others. Statistical analysis was performed to select an optimal combination of acoustic features that affect the emotion from speech. We used a long short-term memory model (LSTM) based model to classify emotions from speech. To compare the proposed method performance, two experiments using K-EmoDB and various international databases were conducted. The results demonstrated that the proposed method shows better performance than previous studies.  
This paper is organized as follows. Section 2 introduces research into existing speech analysis technology. Section 3 introduces the database used in this research. Section 4 explains the proposed emotion recognition method. Section 5 presents the experiment description and results, and then concludes with Section 6.