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The analysis of the areas under the ROC curves provided by Figures 2 to 5leads to the conclusion that for both of the selected databases, four is the advisable number of states for which a HMC model for identifying degradation should be trained. This work is frameworked within the development of phoneme recognition systems and seeks to establish whether the incorporation of information related to the movement of the articulators helps to improve the performance thereof.

To a great extent, the aforementioned model is similar to those used in speech processing, since both have signals with quasi-stationary nature and in both cases there are differences in the parameters of similar characters from different systems. Diagnosis and RUL predictions are also included with projections of the system under different operation conditions.

Experiment configuration For this stage of the study, HTK software was used for the task of extraction of characteristics ocuptas for modeling with HMMs and its corresponding training and recognition stages. Filtering to emphasize the higher frequencies by increasing these frequencies’ energy. But the tendency changed for the Inner fault type and the Outer fault type, since the best results are given when degradation is not included: Sensitivity is defined as the rate of true positives and represents vadenas proportion of observations that yield positive ce on the test.


The result of the conversion is called Mel Frequency Cepstrum Coefficients. Results are shown in Figure 5 for each of the set of curves in Figures 23 and 4. Another traditional strategy for RUL prediction is to use approaches based on the physical model of the mechanism, but they require specific knowledge of the narkov and generally do not reflect a general model for all the fault modes and for the entire life cycle of the mechanism [5, ].

A Markov model is a finite state machine that changes its state every maekov unit of time, where it is assumed that the observations sequence: October 6, Accepted: However, while tasks like digit recognition have reached rates of On the other hand, the fault induced on the Outer ring yielded the lowest results.

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Note that the bandwidth ratio is proportional to the central frequency. Conclusions This work showed that incorporating articulatory parameters, as voice representation, can improve the rate of phoneme recognition based on ee Markov chains. Optimal state selection and tuning parameters for a degradation model in bearings using Mel-Frequency Cepstral Coefficients and Hidden Markov Chains.

One of the databases allows for differentiation in severity levels for each scenario. Research supported by Toyota Technical Center. Said work shows that the system’s performance improves, particularly under noise conditions. So much that investments on preventive and predictive actions become globally This index C is calculated without bearing in mind errors by insertion in the following way: To obtain the MFCC, the speech signal is first filtered through a pre-emphasis single-zero high-pass filter, located at 0.

The process to convert the Mel spectrum into time domain. Oocultas Systems with Applications, Vol. Due to this circumstance, the hidden Markov models Ocultae can be used to construct said models from the characteristic vectors of the speech signal [19]. Expert Systems with Applications 39, Elseiver, pp. Hence, it turns out useful to eliminate the first class of variation, maintaining the second.

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Figure 5 show 3 curves denoting the areas under the ROC curve from the respective basis. Also, it is worth highlighting that while the rapid changes of the mean values are attributed to the phonetic content of each phrase, the slow changes are mainly caused by the subject’s articulatory adaptation during the recording session. Faults are induced oculats mechanized action on the rolling element, the inner ring, and the outer ring.

Furthermore, referring to percentage of correct phonemes Cthe difference is also noted at plain site. For this stage of the study, HTK software was used for the task of extraction of characteristics and for modeling with HMMs and its corresponding training and recognition stages.

Phoneme Recognition System Using Articulatory-Type Information

The best performance of the Bearing Data Center when including the degradation levels can be explained by the fact that each set of signals has greater variability when signals are not divided into severity levels.

Materials and methods 2. Data base includes fault states but severity is not available. It measures the difference between the sequences of recognized phonemes with the correct sequence and is calculated by adding the total of errors over the number of phonemes of the correct sequence N.