{'fraction_non_alphanumeric': 0.016778523489932886, 'fraction_numerical': 0.0, 'mean_word_length': 5.633333333333334, '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}}
Learning phonetic distinctions from speech signals

Previous work has shown that connectionist learning systems can simulate important aspects of the categorization of speech sounds by human and animal listeners. Training is on representations of synthetic, exemplar voiced and unvoiced stop consonants passed through a computational model of the auditory periphery. In this work, we use the modern inductive inference technique of support vector machines (SVMs) as the learning system. Visualization of the SVM’s weight vector reveals what has been learned about the voiced/unvoiced distinction.