Text-dependent speaker recognition using speaker specific compensation

This paper proposes a new method for text-dependent speaker recognition. The scheme is based on learning (what we refer to as) speaker-specific compensators for each speaker in the system. The compensator is essentially a speaker to speaker transformation which enables the recognition of the speech of one speaker through a speaker-dependent speech recognition system built for the other. Such a transformation, adequate for our purposes, may be achieved by a simple vector addition in the cepstral domain. This speaker-specific compensator captures the characteristics of the speaker we wish to recognize. For each speaker who is registered into the system, we learn a unique set of compensators. The speaker recognition decision is then based on which compensator achieves best speech recognition scores.