Journal
ADVANCES IN ENGINEERING SOFTWARE
Volume 180, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.advengsoft.2023.103412
Keywords
Speech; Emotion recognition; Acoustic feature; Spectrogram feature; E-ReliefF; Ensemble classifier; AEUWB
Ask authors/readers for more resources
This research proposes a new automated speech emotion recognition model. It filters the raw speech data and segments the signals into frames. It then recovers the Spectrogram feature using Convolutional Neural Network (CNN). The acoustic and Spectrogram features are combined to create a hybrid feature vector. The model uses an ensemble-of-classifiers consisting of Recurrent Neural Network (RNN), DBN, and Artificial Neural Network (ANN) for speech emotion recognition.
This research intends to propose a new unique and automated speech emotion reorganization model by going through. Initially, the obtained raw speech data is filtered using Butterworth filter and then the signals are segmented into frames. In addition, using the Convolutional Neural Network (CNN), the Spectrogram feature is recovered from the frames. After that, the extracted acoustic and Spectrogram feature is amalgamated to make a hybrid feature vector. Furthermore, the Enhanced-ReliefF (E-ReliefF) is utilized to choose the most important features from the hybrid feature vector. The speech emotion recognition phase is modelled with an ensemble-of-classifiers. The proposed EC consists of a Recurrent Neural Network (RNN), DBN, and an Artificial Neural Network (ANN). The ANN and DBN are trained with the hybrid feature vector. The results of DBN and ANN are fed into an optimized RNN, which will provide the final outcome corresponding to the emotions expressed in the input speech. Furthermore, a RNN weight is adjusted using a hybrid optimization technique to improve speech emotion categorization precision. Arithmetic Exploration updated Wildbeast Model (AEUWB) is a newly presented hybrid optimization model that combines two classic optimization models, namely Arithmetic Optimization Algorithm (AOA) and Wildebeest herd optimization (WHO). Moreover, a comparative analysis validates the projected model's (AEUWB+EC) effectiveness. Accordingly, the accuracy of the presented method is 12.3%, 9.2%, 7.2%, 15.45, 17.5% and 13.4% superior than the conventional models like LA+EC, SSO+EC, SSO+EC, WHO+EC, AOA+EC, respectively, at the 90th LP.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available