{'fraction_non_alphanumeric': 0.021891418563922942, 'fraction_numerical': 0.0017513134851138354, 'mean_word_length': 5.245901639344262, '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}}
Subspace-based speaker-independent vowel recognition

The subspaces representing two different vowel classes may have a large common subspace due to speaker variability, noise and coarticulation. We use common principal component (CPC) and its extension i.e., partial-CPC (pCPC) to obtain a specific subspace for each vowel which is insensitive to variations. We perform CPC analysis on the covariance matrices of the vowels. An input vector from an unknown vowel is classified based on the maximum length of its projection on the specific subspaces. We have chosen 18D mel-frequency cepstral coefficients as a feature in our recognition task. The specific subspace is treated as a transformation matrix which enhances the vowel-specific information in the feature vector and, in turn, increases SNR. Experiments were performed on vowels extracted from a multiple speaker set taken from different dialect regions. The results are encouraging in the context of a speaker-independent framework. Visual analysis of the vowel basis spectra provides useful and interesting information by highlighting the importance of different frequency regions.