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Table 2 shows that LNBNN in combination with MFCCs does not have the highest correction rate.
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An improvement in the system performance is observed by combining other audio features with MFCCs used in the baseline configuration.
Table 4 ASR performance in WERs of MFCCs and their combination with FDLPs modeled by HMM/GMM-, HMM/DNN -,M/DNN)-, and BN-HMM/GMM-based systems on MP-FR evaluation data System HMM/GMM HMM/DNN BN-HMM/GMM MFCC 17.1 14.9 14.5 MFCC+FDLP 16.5 14.4 13.9 The acoustic models are trained on MP-FR (19h) data.
FDLP features are to some extent complementary to MFCCs [53] and the combination of MFCCs and FDLPs yields further improvement.
In conjunction with MFCCs, the following additional features are investigated.
It is hence recommended to incorporate them along with MFCCs.
The overall accuracy of the system was improved up to 89.2% for a combination of MFCCs and aperiodicity features.
The coarse shape of the power spectrum of the recording from the auditory scene is represented with MFCCs.
Comparing this work with other studies mentioned in Section 1, results with MFCCs are coherent with these obtained in [ 17, 21, 36], although methodologies followed in them are different to the one proposed in this study.
Features based on MFCC have been commonly employed in combination with variety of classifiers.
Their combination with Dustin Penner has been potent.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com