{'fraction_non_alphanumeric': 0.017857142857142856, 'fraction_numerical': 0.0015527950310559005, 'mean_word_length': 5.167464114832536, '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}}
A real time speaker identification using artificial neural network

Nowadays it is obvious that speakers can be identified from their voices. In this paper detail of speaker identification from the real-time system point of view is described. Firstly, it have been reviewed the well-known techniques used in speaker identification then the details of every step in identification process and explains the ideas, which leaded to these techniques. We start from the basic definitions used in DSP, then we move to the feature extraction step. Being widely used in pattern recognition tasks, neural networks have also been applied in speaker recognition. In this study, we developed a text-independent speaker identification system based on Back-propagation Neural Network (BPN). BPNs supply flexibility and straightforward design which make the system easily operable along with the successful classification results. In order to analyze the system in practice we made appropriate software and using real data we ran several tests. Empirical results show that proposed approach greatly improves identification speed in feature matching step. From the experiment it is found that the system correctly identify 96% of the speakers, using less then one second of test samples from each speaker.