Profiling vectors for speaker identification
Harry Hollien, Ming Feng Jiang · The Journal of the Acoustical Society of America · 1991
When speaker verification is the issue of interest, it is possible to focus on signal analysis irrespective of the speech related features it contains. Such approaches are appropriate in this case because system distortions are minimal, noise is low, talkers are cooperative, and very sophisticated equipment is available. Not so for speaker identification. Here extensive channel and speaker distortions (including noise) can be expected; speech is noncontemporary and speakers usually uncooperative. Hence, the signal is so distorted or masked, the usual processing techniques cannot be expected to be very useful. The approach to speaker identification demonstrated in this paper is threefold. First, it is assumed that the signal contains speech features that are robust (i.e., resistant to noise and distortion) and unique to the talker. These idiosyncracies are based on speaker's anatomy, physiology, and habitual communicative patterns. Second, it is postulated that, while there may be no single attribute within a person's speech/voice that would permit them to be differentiated from all other speakers under any set of conditions, the simultaneous use of a large series of feature analyses may permit identification. Finally, it has become possible to reduce bias among the vectors by the normalization of data. In turn, this approach leads to a very effective two-dimensional profile wherein the unknown speaker must first be identified and then comparisons made to known talkers. A system of this type has been structured and tested; it is based on four natural speech vectors, each containing 20–40 parameters. Data regarding this general approach and these vectors have been reported previously. This presentation will focus on the effects (on efficient speaker identification in the field) of normalizing the vector data and reducing it to a two-dimensional profile.