{'fraction_non_alphanumeric': 0.017226528854435832, 'fraction_numerical': 0.00516795865633075, 'mean_word_length': 5.419889502762431, '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 variances in robust feature extraction based on a parametric model of speech distortion

This paper presents a technique that exploits the denoised speech’s variance, estimated during the speech feature enhancement process, to improve noise-robust speech recognition. This technique provides an alternative to the Bayesian predictive classiﬁcation decision rule by carrying out an integration over the feature space instead of over the model-parameter space, offering a much simpler system implementation and lower computational cost. We extend our earlier work [5] by using a new approach, based on a parametric model of speech distortion and thus free from the use of any stereo training data, to statistical feature enhancement, for which a novel algorithm for estimating the variance of the enhanced speech features is developed. Experimental evaluation using the fullAurora2 test data sets demonstrates an 11.4% digit error rate reduction averaged over all noisy and SNR conditions, compared with the best technique we have developed [2] prior to this work that did not exploit the variance information and that required no stereo training data.