2 Speaker identification
2.4 Classification/decision taking algorithm

After the feature extraction and possible normalization/ compensation phase (will be presented in the next section) a classification must be used to decide which user (features or models) are the closes to the unknown one. Still it is possible to reject any if the recorded match/ confidence is too low. There are several successful classification techniques that differ in their complexity, way how they work and what they assume upon the processed data. These methods are classified into several major categories each with its pros and cons as follows:

- Non-parametric methods- do not impose any restriction on the data, thus they do not use any model to describe the space. Its main representative is the K-nearest neighbour method (KNN) KNN finds k closest vectors to the unknown one and based on them use some criteria to decide about the result.
- Parameter based methods- assume some structure of the feature space and model it by some parameters. The most successful and frequently used is the mixture of Gaussian distributions. This model is called Gaussian mixture model (GMM). In the lack of data and using proper models that match the space, these methods are superior to the non –parametric one.
- Discriminative methods – try to model/ divide the feature space so that the classification error is as small as possible. It is quite straightforward to do this on the training part of the database but it is trickier to do it (only estimate) for unseen data. In such situation good generalization ability is required (low error for unseen samples). The main representatives of this group are neural networks (NN) and support vector machines (SVM). Both NN and SVM can in certain conditions provide excellent results.
- Generative methods - aim to describe the space as close as possible and do not attempt to separate individual users. If the models perfectly match the data (in reality they don’t) it is possible to construct an optimal classifier that reach the minimal possible cost which was derived by Bayes, thus it is referred to as Bayes classification. Again, the most successful model is the above mentioned GMM.

It was discovered that it is beneficial to use general models of universal speakers that have been created by training samples from many speakers.