{'fraction_non_alphanumeric': 0.014689265536723164, 'fraction_numerical': 0.0022598870056497176, 'mean_word_length': 5.110344827586207, '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}}
Capability of classifying vowels with a residual excited linear prediction (RELP) vocoder

The residual excited linear prediction (RELP) vocoder is a speech codec of good voice quality and a moderate bit rate of 9.6 Kbps for digital communication. However, the RELP does not positively utilize the parameterized speech information to identify speech contents and to determine what word was spoken. This paper proposes a method to classify the vowels in human speech by the RELP vocoder. The method analyzes the frequency response of the LPC filter whose parameters are obtained from a segment of the vowel contained in speech signal. Using the average vectors of frequency response with respect to each vowel sound, mutual Euclidian distances among the vowels were studied. Observed clear separation among the vowels suggests added capability of speech recognition to the RELP vocoder.