{'fraction_non_alphanumeric': 0.023543990086741014, 'fraction_numerical': 0.0024783147459727386, 'mean_word_length': 5.733333333333333, '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}}
Application of voiced-speech variability descriptors to emotion recognition

The following paper examines a possibility of applying phone-pronunciation variability descriptors in emotion classification. The proposed group of descriptors comprises a set of statistical parameters of Poincare maps, which are derived for evolution of formant-frequencies and energy of voiced-speech segments. Poincare maps are represented by means of four different parameters that summarize various aspects of plot's scatter. It has been shown that incorporation of the proposed features into a set of commonly-used emotional-speech descriptors, results in a substantial, ten-percent increase in emotion classification performance - recognition rates are at the order of 80% for six-category, speaker independent experiments.