Normalizations and selection of speech segments for speaker recognition scoring

Normalization and selection techniques are described which improve speaker recognition accuracy using very short uncontrolled speech samples. The first normalization depends on the means and variances of scores for a short, unknown sample matched to different models for many speakers. The selection procedure discards portions of a speech sample with poor speaker-discrimination ability. A second normalization is based on the range of matching scores of the supposed speaker´s model against other speaker´s models. It facilitates setting acceptance thresholds for speaker verification against an open population