02350nas a2200253   4500000000100000000000100001008004100002260001200043653002500055653005000080653002200130653004600152653003100198100002200229700001500251700004000266700001800306245010400324856005800428300000900486490001300495520157400508022001402082       9998                            d  c02/202510aAttention Mechanisms10aConvolutional Peephole Long Short-Term Memory10aFeature Selection10aImproved Jellyfish Optimization Algorithm10aSpeech Emotion Recognition1 aRamya Paramasivam1 aK. Lavanya1 aParameshachari Bidare Divakarachari1 aDavid Camacho00aA Robust Framework for Speech Emotion Recognition Using Attention Based Convolutional Peephole LSTM  uhttps://www.ijimai.org/journal/bibcite/reference/3532  a1-140 vIn press3 aSpeech Emotion Recognition (SER) plays an important role in emotional computing which is widely utilized in various applications related to medical, entertainment and so on. The emotional understanding improvises the user machine interaction with a better responsive nature. The issues faced during SER are existence of relevant features and increased complexity while analyzing of huge datasets. Therefore, this research introduces a wellorganized framework by introducing Improved Jellyfish Optimization Algorithm (IJOA) for feature selection, and classification is performed using Convolutional Peephole Long Short-Term Memory (CP-LSTM) with attention mechanism. The raw data acquisition takes place using five datasets namely, EMO-DB, IEMOCAP, RAVDESS, Surrey Audio-Visual Expressed Emotion (SAVEE) and Crowd-sourced Emotional Multimodal
Actors Dataset (CREMA-D). The undesired partitions are removed from the audio signal during pre-processing and fed into phase of feature extraction using IJOA. Finally, CP LSTM with attention mechanisms is used for emotion classification. As the final stage, classification takes place using CP-LSTM with attention mechanisms. Experimental outcome clearly shows that the proposed CP-LSTM with attention mechanism is more efficient than existing DNN-DHO, DH-AS, D-CNN, CEOAS methods in terms of accuracy. The classification accuracy of the proposed CP-LSTM with attention mechanism for EMO-DB, IEMOCAP, RAVDESS and SAVEE datasets are 99.59%, 99.88%, 99.54% and 98.89%, which is comparably higher than other existing techniques.  a1989-1660