{'fraction_non_alphanumeric': 0.031210986267166042, 'fraction_numerical': 0.011235955056179775, 'mean_word_length': 4.897058823529412, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Emotion Recognition for Romanian Language using Deep Learning with DL-CNN convolutional layers

In this paper, we used the SRoL (Voiced Sounds of the Romanian Language) emotional corpus for Romanian language. We used a Deep Learning Neural Network (DL-CNN) to automatically recognition four emotions: joy, sadness, fury and neutral tone. A 3-layer deep learning neural network is used (two autoencoders are associated to the first two hidden layers and the third layer is a softmax layer). The best results of the emotion recognition obtained is 84,48% with fine tunning and 74.95% without fine tunning. It was noted that the percentages of emotion recognition provided by the MFSC - mel-frequency spectral coefficients are better than those obtained by the MFCC - mel frequency cepstral coefficients.