Age, Gender and Emotion Recognition through Speech Spectrograms using Feature Learning
- Designed a state-of-the-art technique for identifying various speaker characteristics, including age, gender, and emotion, by analyzing sampled audio.
- Analyzed the characteristics of the speech signal, such as MFCC, ZCR and RMS energy.
- Evaluated system's accuracy, resulting in 79.57% and 93.26% accuracy for age and gender classification respectively, and 98% accuracy for emotion recognition.