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This work develops a cross correlation maximization technique, based onstatistical concepts, for pattern matching purposes in time series. Thetechnique analytically quantifies the extent of similitude between a knownsignal within a group of data, by means of a single parameter. Specifically,the method was applied to voice recognition problem, by selecting samples froma given individual recordings of the 5 vowels, in Spanish. The frequency ofacquisition of the data was 11.250 Hz. A certain distinctive interval wasestablished from each vowel time series as a representative test function andit was compared both to itself and to the rest of the vowels by means of analgorithm, for a subsequent graphic illustration of the results.  We conclude that for a minimum distinctive length, the method meetsresemblance between every vowel with itself, and also an irrefutable differencewith the rest of the vowels for an estimate length of 30 points (~2 10-3 s).

A novel method based on cross correlation maximization, for pattern matching by means of a single parameter. Application to the human voice