M.A. Zissman

TL;DR: In this paper, an intrusion detection evaluation test bed was developed which generated normal traffic similar to that on a government site containing 100's of users on 1000's of hosts, and more than 300 instances of 38 different automated attacks were launched against victim UNIX hosts in seven weeks of training data and two weeks of test data.

TL;DR: Four approaches for automatic language identification of speech utterances are compared: Gaussian mixture model (GMM) classification; single-language phone recognition followed by languaged dependent, 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).

TL;DR: The types of performance measurements that are desired and that have been used in the past are explored and suggestions for research directed toward improving the measurement capabilities are presented.

TL;DR: The set of available cues for language identification of speech is described and the different approaches to building working systems are discussed, including a range of historical approaches, contemporary systems that have been evaluated on standard databases, and promising future approaches.

TL;DR: The paper compares the performance of four approaches to automatic language identification of telephone speech messages: Gaussian mixture model classification (GMM), language-independent phoneme recognition followed by language-dependent language modeling (PRLM), parallel PRLM, PRLM-P, and language- dependent parallel phoneme Recognition (PPR).