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However, in order to obtain the best results and collaboration, the process for reporting errors and proposing features should be as transparent as possible and should recognize the effort of the contributor.
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However, the proposed features vary quite widely from one proposal to another.
'Proposed features' section presents the proposed features.
The OPMI features enhance discrimination property of the proposed features.
Figure 20 Performance of proposed features using Gaussian mixture models.
Table 3 Classification results; proposed features extraction method.
We also calculate the proposed features using different distance metrics.
A multi-condition AM was retrained using the proposed features.
Figure 19 Performance of proposed features compared with traditional methods.
The proposed features and classification mechanism are described next.
Evaluating the significance of the proposed features, the maximal accuracy attained using only n-gram features is 91.8% compared to 94.9% using our proposed feature vector.
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