Speech Emotion Recognition Using MFCC Features and LSTM-Based Deep Learning Model
Speech Emotion Recognition (SER) is the use of machines to detect the emotional state of humans based on the speech, which is gaining importance in natural human-computer interaction. Speech is a very valuable source of information, as emotions modify the patterns of speech; pitch, energy and even timing. Nonetheless, SER is not an easy task because speakers are not constant, and situations vary when recording and the sound similarity between specific feelings. In this work, the author introduces a speech emotion recognition system relying on the Mel-Frequency Cepstral Coefficient and Long Short-Term Memory (LSTM) neural network, as a feature extraction method. The Toronto Emotional Speec...
| Authors | Adelekun Oluwademilade, Ademola Adedamola, Abiola Abdulhakeem et al. |
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| Primary category | cs.SD |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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