Recognition of Pathological Voices by Human Factor Cepstral Coefficients (HFCC)
نویسندگان
چکیده
منابع مشابه
Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition.
Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in automatic speech recognition systems, primarily because the coefficients fit well with the assumptions used in hidden Markov models and because of the superior noise robustness of MFCC over alternative feature sets such as linear prediction-based coefficients. The authors have recently introduced human factor...
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This work presents a novel approach for the automatic detection of pathological voices based on fusing the information extracted by means of mel-frequency cepstral coefficients (MFCC) and features derived from the modulation spectra (MS). The system proposed uses a two-stepped classification scheme. First, the MFCC and MS features were used to feed two different and independent classifiers; and...
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In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patter...
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In this paper, we propose a peak-weighted cepstral lifter (PWL) for enhancing the spectral peaks of an all-pole model spectrum in the cepstral domain. The design parameter of the PWL is the degree of pole enhancement or pole shifting toward the unit circle. The optimal pole shifting factor is chosen by considering the sensitivity to spectral resonance peaks, the variability of cepstral variance...
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ژورنال
عنوان ژورنال: Journal of Computer Science
سال: 2020
ISSN: 1549-3636
DOI: 10.3844/jcssp.2020.1085.1099