{'fraction_non_alphanumeric': 0.02925531914893617, 'fraction_numerical': 0.0, 'mean_word_length': 5.605263157894737, '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}}
On the Initialization of Dynamic Models for Speech Features

In this work, a novel approach for the initialization of switching linear dynamic models (SLDMs) as dynamic models for the trajectory of speech features is proposed. Borrowing ideas from the ”k-means++”-algorithm, the goal of this approach is to find distinctly different SLDMs, modelling the complex dynamics of the speech features, already at the initialization stage of a subsequently following ”expectation-maximization (EM)”-algorithm. Experimental results comparing differently initialized SLDMs in a model-based speech feature enhancement scheme show the superiority of the proposed initialization routine in terms of a reduced word error rate on an automatic speech recognition task.