{'fraction_non_alphanumeric': 0.018066847335140017, 'fraction_numerical': 0.004516711833785004, 'mean_word_length': 5.331428571428572, '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}}
Exploiting speaker segmentations for automatic role detection. An application to broadcast news documents

In the field of automatic audiovisual content-based indexing and structuring, finding events like interviews, debates, reports, or live commentaries requires to bridge the gap between low-level feature extraction and such high-level event detection. In our work, we consider that detecting speaker role to enrich interaction sequences between speakers is a first step to reach this goal. The generic method we propose follows a data mining approach. We assume that speaker roles can emerge from parameters extracted from speaker segmentations without taking any prior information into account. Each speaker is then represented by a feature vector carrying temporal, signal and prosodic information. In this paper, we study how methods for dimensionality reduction and classification can help to recognize speaker roles. This method is applied to the corpus of the ESTER2 evaluation campaign and our best result reaches about 72% of well recognized roles that corresponds to nearly 79% of speech time.