Forensic Speaker Identification Using Speech Quality Data

Abstract

The performance of speaker recognition systems based on Gaussian mixture models is often impaired both by the low quality and by the short duration of test speech samples. In literature, a large number of best material selection criteria were described, suitable for the scoring stage in forensic automatic speaker recognition systems. An application of quality-based speaker features is described in the present paper which outperforms forensic speaker recognition systems that assume uniform quality of speech during model training and scoring.