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The models trained with speech data having slow speaking rate would have greater self-loop transition probabilities than the models trained with speech data having fast speaking rate.
Models trained with data sets less then 1500 molecules showed quite diverse predictive performance.
Here, we extend this idea to ensembles composed of models trained with different algorithms (hetero-ensembles).
Compared with speaker models trained with reverberant speech, our method is expected to exhibit a better speaker recognition performance.
Panels (a) to (c) show the results obtained based on acoustic models trained with the clean training data.
An adaptation of multiple reverberant acoustic models trained with different T 60 values is proposed in [23].
So, the models, trained with fast and slow speaking rate speech data, would have different transition probabilities.
Biomass maps were derived using empirical models trained with in-situ above ground biomass data per seagrass species.
The best results for training and test were obtained from the models trained with Bayesian regularization and Levenberg-Marquardt algorithms.
Moreover, our approach was developed to process inaccurate transcriptions as it can handle 'low quality' acoustic models, i.e. non-robust models trained with a small amount ofdata [26].
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