Speech variability in automatic speaker recognition systems for commercial and forensic purposes

Speaker recognition is a major task when security applications through speech input are needed. Nevertheless, speech variability is a main degradation factor in speaker recognition tasks. Both intra-speaker and external variability sources produce mismatch between training and testing phases. In this contribution, channel and inter-session variability are explored in order to accomplish real automatic systems for both commercial and forensic speaker recognition. Results are presented making use of "AHUMADA", a subset of "GAUDI" large speaker recognition-oriented database in Spanish.     

Speaker Recognition The A TVS-UAM System at NIST SRE 05

Automatic speaker recognition systems have been largely dominated by acoustic-spectral-based systems, relying in proper modelling of the short-term vocal tract of speakers. However, there is scientific and intuitive evidence that speaker-specific information is embedded in the speech signal in multiple short- and long-term characteristics. In this work, a multilevel speaker recognition system combining acoustic, phonotactic, and prosodic subsystems is presented and assessed by blind submission to NIST 2005 Speaker Recognition Evaluation