{'fraction_non_alphanumeric': 0.019345238095238096, 'fraction_numerical': 0.00744047619047619, 'mean_word_length': 5.349056603773585, '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}}
Channel frequency response correction for speaker recognition

While some powerful techniques have been developed for speaker recognition tasks [1], reliable decisions in a telephone environment can still be quite elusive. For example, using an in-house telephone database of long distance phone conversations, a GMM-UBM with 30 seconds for both training and testing yielded only a performance rate of 56%. Part of the difficulty with speaker identification for telephone applications is the variations and distortions in the telephone networks. This paper uses a blind channel estimate of the audio channel to modify the mel-features used in speaker identification tasks.