Speech Emotion Recognition Using MFCC Features and LSTM-Based Deep Learning Model

📰 ArXiv cs.AI

Learn to recognize emotions in speech using MFCC features and LSTM-based deep learning models, improving human-computer interaction

intermediate Published 30 Apr 2026

Action Steps

Extract MFCC features from speech data using Python libraries like Librosa
Configure an LSTM-based deep learning model using Keras or TensorFlow to recognize emotions
Train the model on a labeled dataset of speech samples with corresponding emotions
Test the model on a separate dataset to evaluate its accuracy
Fine-tune the model by adjusting hyperparameters and experimenting with different architectures

Who Needs to Know This

Data scientists and AI engineers working on speech recognition and natural language processing tasks can benefit from this research, as it enhances the accuracy of emotion detection in speech

Key Insight

💡 MFCC features and LSTM-based models can effectively detect emotions in speech, enabling more natural human-computer interaction

Key Takeaways

Learn to recognize emotions in speech using MFCC features and LSTM-based deep learning models, improving human-computer interaction

Full Article

Title: Speech Emotion Recognition Using MFCC Features and LSTM-Based Deep Learning Model

Abstract:
arXiv:2604.25938v1 Announce Type: cross Abstract: 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 spe

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