Classical parameterization techniques in Speaker Identification tasks use the codification of the power spectral density of speech as a whole, not discriminating between articulatory features due to the dynamics of vocal tract (acoustic-phonetics) and those contributed by the glottal source. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation gesture. In this way information which is conveyed in both components depending in different degree on message and biometry is estimated and treated differently to be fused at the time of template composition. The methodology to separate both components is based on the decorrelation hypothesis between vocal and glottal information and it is carried out using Joint Process Estimation. This methodology is briefly discussed and its application on vowel-like speech is presented as an example to observe the resulting estimates both in the time as in the frequency domain. The parameterization methodology to produce representative templates of the glottal and vocal components is also described. Speaker Identification experiments conducted on a wide database of 240 speakers is also given with comparative scorings obtained using different parameterization strategies. The results confirm the better performance of de-coupled parameterization techniques compared against approaches based on full speech parameterization
