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 will be explored in order to accomplish real automatic systems for both commercial and forensic speaker recognition. Results will be presented making use of "AHUMADA," a subset of "GAUDI" large speaker recognition-oriented database in Spanish.