Table 3 Overview of emotion recognition based on speech
From: Development and application of emotion recognition technology — a systematic literature review
No | Author & year | Feature extraction | N | Emotions | Test sample | Pattern recognition methods | Accuracy |
|---|---|---|---|---|---|---|---|
1 | Chin, Kuan-Chen (2021) | 1MFCC | 337 | Stability, Unstable | Scheduling records of out-of-hospital cardiac arrest | SVM | 92.87% |
2 | Ning JIA (2021) | Speed of speech, short-term averagy anegy, pitch frequence, MFCC | NA | Depression | AVi-D dataset | 2GAN, 3CNN | 67% |
3 | Rejaibi, Emna (2022) | Spectral, Cepstral, Glottis,Prosodic, Voice Quality, MFCC | NA | Depression | DAIC-WOZ dataset, RAVDESS dataset, AVi-D dataset | 4DL | 76.27% |