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LSTM model achieves 99% accuracy in speech emotion recognition

By PulseAugur Editorial · [1 sources] ·

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 →

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

LSTM model achieves 99% accuracy in speech emotion recognition

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This is a research paper detailing a new model for speech emotion recognition.
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Adelekun Oluwademilade, Ademola Adedamola, Abiola Abdulhakeem, Akinpelu Azeezat, Eraiyetan Israel, Omotosho Oluwadunsin, Ibenye Ikechukwu, Ayuba Muhammad, Olusanya Olamide, Kamorudeen Amuda ·

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

    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 informatio…

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