This PhD thesis tries to understand how to analyse, decompose, model and transform the vocal identity of a human when seen through an automatic speaker recognition application. It starts with an introduction explaining the properties of the speech signal and the basis of the automatic speaker recognition. Then, the errors of an operating speaker recognition application are analysed. From the deficiencies and mistakes noticed in the running application, some observations cm be made which will imply a re-evaluation of the characteristic parameters of a speaker, and to reconsider some parts of the automatic speaker recognition chain. In order to determine what are the characterising parameters of a speaker, these are extracted from the speech signal with an analysis and synthesis harmonic plus noise model (H+N). The analysis and re-synthesis of the harmonic and noise parts indicate those which are speech or speaker dependent. It is then shown that the speaker discriminating information can be found in the residual of the subtraction from the original signal of the H+N modeled signal. Then, a study of the impostors phenomenon, essential in the tuning of a speaker recognition system, is carried out. The impostors are simulated in two ways: first by a transformation of the speech of a source speaker (the impostor) to the speech of a target speaker (the client) using the parameters extracted from the H+N model. This way of transforming the parameters is efficient as the false acceptance rate grows from 4% to 23%. Second, an automatic imposture by speech sepent concatenation is carried out. In this case the false acceptance rate grows to 30%. A way to become less sensitive to the spectral modification impostures is to remove the harmonic part or even the noise part modeled by the H+N from the original signal. Using such a subtraction decreases the false acceptance rate to 8% even if transformed impostors are used. To overcome the lack of training data — one of the main cause of modeling errors in speaker recognition — a decomposition of the recognition task into a set of binary classifiers is proposed. A classifier matrix is built and each of its elements has to classify word by word the data coming from the client and another speaker (named here an anti-speaker, randomly chosen from an extemal database). With such an approach it is possible to weight the results according to the vocabulary or the neighbours of the client in the parameter (acoustic) space. The output of the mamx classifiers are then weighted and mixed in order to produce a single output score. The weights are estimated on validation data, and if the weighting is done properly, the binary pair speaker recognition system gives better results than a state of the an HMM based system. In order to set a point of operation (i.e. a point on the COR cuwe) for the speaker recognition application, an a priori threshold has to be determined. Theoretically the threshold should be speaker independent when stochastic models are used. However, practical experiments show that this is not the case, as due to modeling mismatch the threshold becomes speaker and utterance length dependant. A theoretical framework showing how to adjust the threshold using the local likelihood ratio is then developed. Finally, a last modeling error correction method using decision fusion is proposed. Some practical experiments show the advantages and drawbacks of the fusion approach in speaker recognition applications.