As we briefly mentioned in the introduction, speaker recognition covers two different areas: on the one hand, speaker identification, on the other hand, speaker verification . As [Doddington (1985)] describes it, the goal of a speaker identification task is to classify an unlabelled voice token as belonging to one of a set of n reference speakers, whereas the speaker verification task is to decide whether or not the unlabelled voice belongs to a specific reference speaker.
Speaker identification is therefore a 1 out of n decision, in the case of closed-set speaker identification , the result of which is an identity assignment to an applicant speaker . However, in practical applications, open-set speaker identification requires an additional outcome of rejection , corresponding to the possibility that the unlabelled speech token does not belong to any of the registered speakers . In such circumstances, the applicant speaker is called an impostor.
Speaker verification can be viewed as a particular case of open-set speaker identification, corresponding to n = 1. The speaker verification system takes a test voice sample and a claimed identity as input, and returns a binary decision: acceptance if the applicant speaker is considered to be the genuine speaker or rejection if he is considered to be an impostor (as regards the claimed identity).
Conversely, open-set speaker identification can be understood as a step of closed-set speaker identification , followed by a step of speaker verification, the latter using the identity assigned by the former, as the claimed identity.
Beyond this major distinction between identification and verification, other related tasks can be mentioned.
For closed-set speaker identification, a misclassification error occurs when a registered speaker is mistaken for another registered speaker (the mistaken speaker).
For speaker verification , two types of error must be distinguished: false rejection when a genuine speaker is rejected and false acceptance when an impostor is accepted as the speaker he claimed he was (the violated speaker ).
For open-set identification, the three types of error can occur. Usually, misclassifications and false acceptances are considered as equally harmful, and therefore merged together. However, these two types of error may not have the same consequences in some practical applications.