Designing and Implementing of Intelligent Emotional Speech Recognition with Wavelet and Neural Network

Abstract

Abstract-Recognition of emotion from speech is a significant subject in man-machine fields. In this study, speech signal has analyzed in order to create a recognition system which is able to recognize human emotion and a new set of characteristic has proposed in time, frequency and time-frequency domain in order to increase the accuracy. After extracting features of Pitch, MFCC, Wavelet, ZCR and Energy, neural networks classify four emotions of EMO-DB and SAVEE databases. Combination of features for two emotions in EMO-DB database is 100%, for three emotions is 98.48% and for four emotions is 90% due to the variety of speech, existing more spoken words and distinguishing male and female which is better than the result of SAVEE database. In SAVEE database, accuracy is 97.83% for two emotions of happy and sad, 84.75% for three emotions of angry, normal and sad and 77.78% for four emotions of happy, angry, sad and normal

Design and Implementation of Fast Spoken Foul Language Recognition with Different End-to-End Deep Neural Network Architectures

“…Additionally, Linear Prediction Coefficients (LPC) and Linear Spectral frequencies (LSF) were used for different applications like speaker recognition [15], spoken digits recognition [16], and emotion recognition from speech [17]. Discrete Wavelet Transform is another feature extraction technique that has been used for speaker recognition [18], speech semantic and emotions recognitions [19,20]. Furthermore, spectrogram images are the best choice of speech and acoustic feature extraction that is suitable for CNN models.…”