{'fraction_non_alphanumeric': 0.022988505747126436, 'fraction_numerical': 0.012539184952978056, 'mean_word_length': 4.9875, '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}}
Noise Robust Front-end for ASR using Spectral Subtraction , Spectral Flooring and Cumulative Distribution Mapping

In this paper, a novel and noise robust front-end based on the combined application of spectral subtraction, spectral flooring and cumulative distribution mapping is proposed. Recognition experiments with the Aurora II connected digits reveal that the proposed front-end achieves an average digit accuracy of 81.46% for a model set trained from clean data and 89.54% for a model set trained from data with various noise conditions. With reference to the ETSI standard Mel-cepstral front-end, the proposed front-end obtains a relative error reduction of around 52% for the clean model set and 14% for the multi-condition model set. Moreover, it is observed that the use of a single fixed parameter to control spectral flooring is beneficial only when cumulative distribution mapping is also applied at a later stage of the frontend processing.