Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition
In this paper, a novel approach, which is based on attention guided 3D convolutional neural
networks (CNN)-long short-term memory (LSTM) model, is proposed for speech based
emotion recognition. The proposed attention guided 3D CNN-LSTM model is trained in end-
to-end fashion. The input speech signals are initially resampled and pre-processed for noise
removing and emphasizing the high frequencies. Then, spectrogram, Mel-frequency cepstral
coefficient (MFCC), cochleagram and fractal dimension methods are used to convert the …
networks (CNN)-long short-term memory (LSTM) model, is proposed for speech based
emotion recognition. The proposed attention guided 3D CNN-LSTM model is trained in end-
to-end fashion. The input speech signals are initially resampled and pre-processed for noise
removing and emphasizing the high frequencies. Then, spectrogram, Mel-frequency cepstral
coefficient (MFCC), cochleagram and fractal dimension methods are used to convert the …
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