Frontiers in Robotics Automation and Control by Alexander Zemliak

By Alexander Zemliak

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Shewhart Graph for total time of test runs. The Shewhart graph contains a Central Line (CL), a Lower Control Line (LCL), an Upper Control Line (UCL) and values related to the issue that we intend to control or improve. In our case, these values are related to the total test time of initial test runs. These data is sequentially plotted along the time, so that we have a historical registry of this information. This is a continuous process and the more data we have, the better will be our Baseline.

The test started with only one vowel (the most effective) and successively other vowels were added one by one according to their individual effectiveness as ranked in Table 2. 4% using all the five vowels simultaneously. No. 4 Table 3. Speaker identification rate depending on number of vowels used. 2 Effectiveness of Speech Features in Speaker Recognition In order to see which features are effective for speaker recognition, we studied here the following six parametric representations: 1) autocorrelation coefficients; 2) linear prediction (LP) coefficients; 3) log area ratios; 4) cepstral coefficients; 5) mel-cepstral coefficients; 6) line spectral-pair (LSP) frequencies.

The test started with only one vowel (the most effective) and successively other vowels were added one by one according to their individual effectiveness as ranked in Table 2. 4% using all the five vowels simultaneously. No. 4 Table 3. Speaker identification rate depending on number of vowels used. 2 Effectiveness of Speech Features in Speaker Recognition In order to see which features are effective for speaker recognition, we studied here the following six parametric representations: 1) autocorrelation coefficients; 2) linear prediction (LP) coefficients; 3) log area ratios; 4) cepstral coefficients; 5) mel-cepstral coefficients; 6) line spectral-pair (LSP) frequencies.

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