Speech Emotion Recognition Using CNN and LSTM

Dhavale, Mansi; Bhandari, Prof.Sheetal

doi:10.1109/iccubea54992.2022.10010751

Cited by 3 publications

(1 citation statement)

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“…In paper [18], it was mentioned that LSTM and CNN can be applied to speech emotion recognition to identify emotions like neutral, anger, sadness, and happiness. Paper [19] proposed an integrated model, CNN_LSTM, which achieved an accuracy of 70.56% using MFCC.…”

Section: Introductionmentioning

confidence: 99%

Research on Speech Emotion Recognition Based on Teager Energy Operator Coefficients and Inverted MFCC Feature Fusion

Wang,

Shen

2023

Electronics

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As an important part of our daily life, speech has a great impact on the way people communicate. The Mel filter bank used in the extraction process of MFCC has a better ability to process the low-frequency component of a speech signal, but it weakens the emotional information contained in the high-frequency part of the speech signal. We used the inverted Mel filter bank to enhance the feature processing of the high-frequency part of the speech signal to obtain the IMFCC coefficients and fuse the MFCC features in order to obtain I_MFCC. Finally, to more accurately characterize emotional traits, we combined the Teager energy operator coefficients (TEOC) and the I_MFCC to obtain TEOC&I_MFCC and input it into the CNN_LSTM neural network. Experimental results on RAVDESS show that the feature fusion using Teager energy operator coefficients and I_MFCC has a higher emotion recognition accuracy, and the system achieves 92.99% weighted accuracy (WA) and 92.88% unweighted accuracy (UA).

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Section: Introductionmentioning

confidence: 99%