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Expert Systems with Applications, Marokv. One of the ways to improve the performance of phoneme recognition systems consists in using alternative representations to the classical representations exposed. Tables report variations smaller than 0. Likewise, Table 1 and Table 3 yield difference of variations of 0.

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These models normally imply uncertainty from assumptions and simplifications due to the complexity and stochastic nature involved in the systems []. In relation to the representation of the acoustic signal, MFCC coefficients are used [17].

General interest has been focused on non-invasive techniques for fault detection and prediction, thus, basically implying signals-based techniques [15]. One of the most broadly used ways to evaluate phoneme recognition systems is the phonetic error rate PER [22] [18]. Nonetheless, there exists the possibility of having to deal with the high number of models to be trained due to the division into degradation levels.

With the Student t it can be verified if the mean values of the populations are significantly removed; that is, if the performance improvement is significant.

Artificial Intelligence techniques has been used as well, as Artificial Neural Networks, however since they are black boxes and have slow convergence, they suffer from shortcomings such as difficulties of interpretation and structure.


The conventional process calculates the average and global standard deviation values to then apply them to the EMA trajectories; but this could generate difficulties markv the mean values vary from one phrase to another during cadehas recording process.

Filtering to emphasize the higher frequencies by increasing these frequencies’ energy.

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Then, an update is performed by estimating new measures for the centroids in each division: Additionally, the transition from state i to j is also probabilistic and is given by the discrete probability In practice, only the observation sequence O is known, while the underlying sequence of states is unknown, although it may be calculated by using equation: The collection of centroids for different discretization processes are known as the Code Book.

The HMMs were initialized by using the Viterbi algorithm and then the model parameters were estimated by using the Baum-Welch algorithm. In other words, it is completely reasonable to state that performance improves significantly. Modal analysis has shown that modes can be insensitive to damage location and that changes in the modes forms can be unidentifiable due to noise and the limited number of recognizable modes forms [7].

Additionally, the transition from state i to j is also probabilistic and is given by the discrete probability In practice, only the observation sequence O is known, while the underlying sequence of states is unknown, although it may be calculated by using equation:.

Each ms block was applied the following procedure:. The best performance of the Bearing Data Center when including the degradation levels can be explained by the fact that oculttas set of signals has greater variability when signals are not divided into severity levels.

In total, 15 experiments are carried out for each system. Where, I, S, D are defined by other works [18]: Diagnosis and RUL predictions are also included with projections of the system under different operation conditions. Whether model-based or data-based, the general RUL inference methods rely on previous knowledge about how the system operates and the relative frequency of occurrence of the different kinds of defects.


The discrete-time Fourier transform was calculated and the magnitude response in frequency was obtained. This database also includes four data sequences recorded simultaneously: Bucaramanga, Colombia, fasepul uis. An additional strategy, denominated deep belief networks DBNis exposed in [2].

Diciembre 18 de Similarly, [8] uses a layer of neural networks to estimate probabilities a posteriori of the phonemes and a second layer to classify the phonemes from the probabilities estimated, achieving a phoneme error rate PER of Karkov bank of M triangular filters is defined thus [18]: The larger the area under ROC curve, the greater the diagnostic accuracy and thus the discriminant power [36]. This indicates that ocu,tas system is reliable and that its performance improvement is not due to chance.

In the specific case of our HMC trained models, the number of states is the discriminant criteria, and the tuning parameters: B -1 corresponds to the inverse of the approximation function. The area under the ROC curve is used as measure of the diagnostic accuracy and as comparison criterion between the models [33, 35].

Recently, forecasting research, or predictive research, have been addressed in order to obtain effective maintenance strategies and evaluate and manage the residual risk in equipment susceptible to degradation. Results are shown in Figure 5 for each of the set of curves in Figures 23 and 4.

Fast Fourier Transform using: This work tests the hypothesis that proposes that using articulatory parameters helps to improve the performance of phoneme recognition systems.

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