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This means that the single-Gaussian HMM set is trained on unwarped features and tested on warped features for different warps throughout the search space.
Assume that we warp the spectra of the th speaker by different warping factors (i.e., ) and compute the warped features over time as.
The canonical HMMs are trained from warped features which are extracted using appropriate self-normalization warps.
The best warp factor for speaker is estimated by maximizing the likelihood of the warped features with respect to the HMM model and transcriptions : (9).
The warped features are generated by transforming the frequency axis by a suitable warping function (speaker-dependent BLT in our case), the models, on the other hand, are trained on unwarped features.
The most popular way of estimating VTLN warps is to use likelihood-based estimation techniques [6, 7] in which a set of HMM models trained on a large population of speakers by placing 1 Gaussian per state is scored against warped features.
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Consequently, this row-wise downsampling can better exploit the warping feature of the DIBR technique and, as a result, can enhance the upsampling performance.
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