Ralf Schlüter studied physics at RWTH Aachen University, Germany, and Edinburgh University, Scotland. He received the Diplom degree with honors in physics in 1995 and the Dr.rer.nat. degree with honors in computer science in 2000, from RWTH Aachen University. From November 1995 to April 1996 Ralf Schlüter was with the Institute for Theoretical Physics B at RWTH Aachen, where he worked on statistical physics and stochastic simulation techniques. Since May 1996 Ralf Schlüter is with the Faculty of Mathematics, Computer Science and Natural Sciences of RWTH Aachen University, where he currently is Academic Director. He leads the automatic speech recognition group at the Human Language Technology and Pattern Recognition lab. His research interests cover speech recognition in general, discriminative training, neural networks, information theory, stochastic modeling, signal analysis, and theoretic aspects of pattern classification. His talk will take place on Tuesday, August 23rd, 2016, 10am in room A112.

On the Relation between Error Measures, Statistical Modeling, and Decision Rules

Abstract: The aim of automatic speech recognition (ASR), or more generally, pattern classification, is to minimize the expected error rate.  This requires a consistent interaction of the error measure with statistical modeling and the corresponding decision rule. Nevertheless, the error measure often is not considered consistently in ASR:

• error measures usually are not easily tractable due to their discrete nature,
• the quantitative relation between modeling and error measure at least analytically is unclear and usually is only exploited empirically,
• the standard decision rule does not consider word error loss.

In this presentation, bounds on the classification error will be presented that can directly be related to acoustic and language modeling. A first analytic relation between language model perplexity and sentence error is established, and the quantitative effect of context reduction and feature omission on the error rate are derived. The corresponding error bounds were discovered and finally analytically proven within a simulation-induced framework, which will be outlined. Also, first attempts on how to design a training criterion to support the use of the standard decision rule while retaining the target of minimum word error rate are discussed. Finally, conditions will be presented under which the standard decision rule does in fact implicitly optimize word/token error rate in spite of its sentence/segment-based target.