An Introduction to Application-Independent Evaluation of Speaker Recognition Systems

In the evaluation of speaker recognition systems - an important part of speaker classification [1], the trade-off between missed speakers and false alarms has always been an important diagnostic tool. NIST has defined the task of speaker detection with the associated Detection Cost Function (DCF) to evaluate performance, and introduced the DET-plot [2] as a diagnostic tool. Since the first evaluation in 1996, these evaluation tools have been embraced by the research community. Although it is an excellent measure, the DCF has the limitation that it has parameters that imply a particular application of the speaker detection technology. In this chapter we introduce an evaluation measure that instead averages detection performance over application types. This metric, CIIr, was first introduced in 2004 by one of the authors [3]. Here we introduce the subject with a minimum of mathematical detail, concentrating on the various interpretations of CIIr and its practical application. We will emphasize the difference between discrimination abilities of a speaker detector ('the position/shape of the DET-curve'), and the calibration of the detector ('how well was the threshold set'). If speaker detectors can be built to output well-calibrated log-likelihood-ratio scores, such detectors can be said to have an application-independent calibration. The proposed metric CIIr can properly evaluate the discrimination abilities of the log-likelihood-ratio scores, as well as the quality of the calibration. © Springer-Verlag Berlin Heidelberg 2007

Fictive motion extraction and classification

Fictive motion (e.g. ‘The highway runs along the coast’) is a pervasive phenomenon in language that can imply both a staticand a moving observer. In a corpus of alpine narratives, it is used in three types of spatial descriptions: conveying the actual motion of the observer, describing a vista and communicating encyclopaedic spatial knowledge. This study takes a knowledge-based approach to develop rules for automated extraction and classification of these types based on an annotated corpus of fictive motion instances. In particular, we identify the differences in the set of concepts involved into the production of the three types of descriptions, followed by their linguistic operationalization. Based on that, we build a set of rules that classify fictive motion with an overall precision of 0.87 and recall of 0.71. The article highlights the importance of examining spatially rich, naturally occurring corpora for the lines of work dealing with the automated interpretation of spatial information in texts, as well as, more broadly, investigation of spatial language involved into various types of spatial discourse