# Design of a Multi-Condition Emotional Speech Synthesizer
## 3 Korean Emotional Speech Database  
The study of emotion analysis has rapidly developed over the last decade with broad interest from researchers in neuroscience, computer science, psychiatry, psychology, audiology, and computer science. The key of these studies is to secure the availability of validated and reliable expressions of emotion. Most emotion datasets include either facial expressions or speech recordings. Among the datasets, few contain audio-visual recordings of speakers in Korean. This study constructs a Korean emotional speech database and re-ports validity and reliability of the data based on ratings from participants. The database was recorded with Korean utterances from professional actors. All recorded data were recorded in a professional studio, considering the sound quality of the data by eliminating any background noise.  
The database was recorded using Korean scripts from dramas and movies, with four professional actors, two females and two males. The scripts were collected from actual scripts used in dramas and movies. The scripts consisted of 30-s-long conversations between a male and a female per emotional scene. We defined four emotions: Anger, Happiness, Neutrality, and Sadness. The scripts consisted of 120 scenes per emotion. As  
Figure 2: Structure of the global style tokens Tacotron [8].  
suggested in [15], actors engaged in their role during recording may provide a more natural expression of the emotions, and emotions are incorporated into the scripts themselves, allowing an actor to record with strong emotions. This database relied on professional actors and senior students from the Drama Department. Actors had Korean as their first language, spoke with a neutral Seoul accent, and did not possess any distinctive features. The actors recorded their voices from around 30 cm away from the microphone (Figure 3). To allow the actors to understand which emotion was requested, a description and sample of each emotion were given. Actors were asked to maintain strong intensity during recording, and feedback was given if a research assistant considered a production to be ambiguous. Trials were organized by emotion, with low-intensity emotions followed by intense emotions. This ordering allowed actors to enter and remain within the desired state for all productions of that emotional category. Actors were then given time to prepare their emotional state using their desired induction technique. We focused on increasing a number of emotional speech data rather than the emotional quality, so we did not carry out the evaluation test, unlike general speech databases.  
After recording, the data were manually segmented by dialog turn (speaker turn) from continuous segments in which the actors were actively speaking. The scripts were segmented into sentences in advance, and used as references with which to split the data. The final audio recordings were divided into approximately 3-10-s-long files. Each file was around two to three hours long per actor, producing a total of around 4000-5000 files. The total size of the database was 18,324 files. All speech emotion data was recorded at 48 kHz and downsampled with a 24 kHz sampling rate in PCM signed 16-bit format. We carried out post-processing, which normalized the amplitude of the signal and removed the front and back margin. The results of post-processing are shown as Figure 4.