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MFCCMel Frequency Cepstral Coefficients (Speech Processing)
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References in periodicals archive ?
MFCC are the most used features for the speaker and speech recognition systems.
The most commonly used feature in sound recognition is Mel frequency cepstrum coefficient (MFCC), which is widely applied in semantic recognition and voice recognition; however its recognition rate is not ideal.
The speech feature selected in this paper is the traditional MFCC feature and the improved GFCC feature.
The basic process is to extract the speech MFCC feature sequence and use the training data to calculate the model parameters and obtain the individual GMM template.
MFCC was one of the most frequent spectral features utilized in speech parameters, and the classification outcomes were statistically significant in identifying depression [22-25].
We are using Gaussian mixture models in order to statistically fit MFCC and spectrogram coefficient evolution over time to a PDF.
[7] developed a Generic and Scalable Architecture for a Large Acoustic Model and Large Vocabulary Speech using MFCC feature extraction.
En [13] se presenta un sistema no invasivo con una tasa de acierto del 99.44%, usando bancos de filtros de coeficientes cepstrales en escala de Mel (MFCC) y un clasificador HMM (Hidden Markov Model); la base de datos utilizada fue la Massachusetts Eye and Ear Infirmary (MEII).
On the other hand, other alternatives propose an MFCC analysis for the feature extraction.
Usually, the short vector is extracted by Mel frequency cepstral coefficient (MFCC) method [4].
Moreover, it is the foundation of many other features such as the MFCC and frequency characteristics.