  Using mel-spectrograms over conventional MFCCs features, we assess the
abilities of convolutional neural networks to accurately recognize and classify
emotions from speech data. We introduce FSER, a speech emotion recognition
model trained on four valid speech databases, achieving a high-classification
accuracy of 95,05\%, over 8 different emotion classes: anger, anxiety, calm,
disgust, happiness, neutral, sadness, surprise. On each benchmark dataset, FSER
outperforms the best models introduced so far, achieving a state-of-the-art
performance. We show that FSER stays reliable, independently of the language,
sex identity, and any other external factor. Additionally, we describe how FSER
could potentially be used to improve mental and emotional health care and how
our analysis and findings serve as guidelines and benchmarks for further works
in the same direction.
