Exact(1)
When 16 Gaussian mixtures were used, the MFCC-based GMM algorithm is performed with 92.0% accuracy.
Similar(7)
The results show significant improvements in classification performance for all noise conditions when these features were used to complement the MFCC and MFCC features.
Likewise, MFCC-based features were used to train HMM classifiers in [16] and reported high classification rates.
The MFCCs are the ground of the baseline system and were used for comparison due to their wide use in speech technology applications.
For word-based speech recognition, 13-dimensional MFCCs with cepstral mean and variance normalization (CMVN) applied were used as acoustic features.
It is worth noting that the proposed approach is outperformed by the MFCC-based approach in only 4 out of the 36 cases.
The primary decision is executed by the MFCC-based GMM algorithm.
MFCC has been used in the feature extraction process in [36] and [32] for localization and classification.
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