Calibration of binary and multiclass probabilistic classifiers in automatic speaker and language recognition
|Niko Brummer (Agnitio)|
Automatic pattern classifiers that output soft, probabilistic classifications---rather than hard decisions---can be more widely and more profitably applied, provided the probabilistic output is well-calibrated. In the fields of automatic speaker recognition and automatic spoken language recognition, the regular NIST technology evaluations have placed a strong emphasis on cost effective application and therefore on calibration. This talk will describe calibration solutions for these technologies, with emphasis on criteria for measuring the goodness of calibration---if we can measure it, we can also optimize it.
Niko Brummer received B.Eng (1986), M.Eng (1988) and Ph.D. (2010) degrees, all in electronic engineering, from Stellenbosch University. He worked as researcher at DataFusion (later called Spescom DataVoice) and is currently chief scientist at AGNITIO. Most of his research for the last two decades has been applied to automatic speaker and language recognition and he has been participating in most of the NIST SRE and LRE evaluations in these technologies, from the year 2000 to the present. He has been contributing to the Odyssey Workshop series since 2001 and was organizer of Odyssey 2008 in Stellenbosch. His FoCal Toolkit is widely used for fusion and calibration in speaker and language recognition research.
His research interests include development of new algorithms for speaker and language recognition, as well as evaluation methodologies for these technologies. In both cases, his emphasis is on probabilistic modelling. He has worked with both generative (eigenchannel, JFA, i-vector PLDA) and discriminative (system fusion, discriminative JFA and PLDA) recognizers. In evaluation, his focus is on judging the goodness of classifiers that produce probabilistic outputs in the form of well calibrated class likelihoods.