LSTM model achieves 99% accuracy in speech emotion recognition

Researchers have developed a novel speech emotion recognition system utilizing Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction and a Long Short-Term Memory (LSTM) neural network for classification. This approach demonstrated a 99% accuracy rate, outperforming a Support Vector Machine baseline which achieved 98% accuracy. The system shows promise for applications such as virtual assistants and mental health monitoring. AI

IMPACT This research advances speech emotion recognition accuracy, potentially improving human-computer interaction in virtual assistants and mental health applications.

RANK_REASON This is a research paper detailing a new model for speech emotion recognition.

Read on arXiv cs.AI →

• Kamorudeen Amuda
• LSTM
• SVM
• TESS
• RBF kernel

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →