Vowel detection using a perceptually-enhanced spectrum matching conditioned to phonetic context and speaker identity

Experiments are separately conducted on two standard continuous speech corpora, a Persian corpus (FARSDAT) and an English one (TIMIT). Given various experiments, it is found that all characteristics employed in the proposed model decrease the total error measure with different degrees. Using the proposed method, the total error values of 14.2% and 18.9% are obtained in clean conditions for FARSDAT and TIMIT, respectively. Moreover, the effectiveness of the proposed algorithm is verified in additive noise conditions with different signal-to-noise ratios. According to the evaluation results, the proposed method shows a desirable performance in terms of the total error in comparison with the existing well-known methods on both corpora and both clean and noisy conditions.