{'fraction_non_alphanumeric': 0.017015706806282723, 'fraction_numerical': 0.0, 'mean_word_length': 5.322314049586777, '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}}
N-gram nearest neighbor algorithm for voice password system

A specific issue in the voice password system is addressed in this paper: When the text content of target speaker's enrollment password has been already known by imposters, they can do a well-behaved impersonation using the same text content as the target speaker. This results in a much higher false acceptance than the traditional voice password system. N-gram based nearest neighbor algorithm is proposed here to improve the speaker detection accuracy. Furthermore, correlation coefficient is adopted as the distance measurement between two acoustic features instead of the traditional Euclidean distance. Experimental results show that the proposed method outperforms the DTW and GMM-UBM algorithms.