ABSTRACT

Abstruct-We have compared the performance of four approaches for automatic language identification of speech utterances: Gaussian mixture model (GMM) classification; single-language phone recognition followed by languagedependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses multiple single-language phone recognizers, each trained in a different language; and languagedependent parallel phone recognition (PPR). These approaches, which span a wide range of training requirements and levels of recognition complexity, were evaluated with the Oregon Graduate Institute Multi-Language Telephone Speech Corpus. Systems containing phone recognizers performed better than the simpler GMM classifier. The top-performing system was parallel PRLM, which exhibited an error rate of 2% for 45-s utterances and 5% for 10-s utterances in two-language, closed-set, forcedchoice classification. The error rate for 11-language, closed-set, forced-choice classification was 11 % for 45-s utterances and 21% for 10-s utterances.

While intrusion detection systems are becoming ubiquitous defenses in today's networks, currently we have no comprehensive and scientifically rigorous methodology to test the effectiveness of these systems. This paper explores the types of performa nce measurements that are desired and that have been used in the past. We review many past evaluations that have been designed to assess these metrics. We also discuss the hurdles that have blocked successful measurements in this area and present suggestions for research directed toward improving our measurement capabilities.

No abstract