We investigate the incorporation of larger time-scale information, such as prosody, into standard speaker ID systems. Our study is based on the Extended Data Task of the NIST 2001 Speaker ID evaluation, which provides much more test and training data than has traditionally been available to similar speaker ID investigations. In addition, we have had access to a detailed prosodic feature database of Switchboard-I conversations, including data not previously applied to speaker ID. We describe two baseline acoustic systems, an approach using Gaussian Mixture Models, and an LVCSR-based speaker ID system. These results are compared to and combined with two larger time-scale systems: a system based on an "idiolect" language model, and a system making use of the contents of the prosody database. We find that, with sufficient test and training data, suprasegmental information can significantly enhance the performance of traditional speaker ID systems.

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In this paper we present the first implementation of LING-STAT, an interactive machine translation system designed to increase the productivity of a user, with little knowledge of the source language, in translating or extracting information from foreign language documents. In its final form, LING-STAT will make use of statistical information gathered from parallel and single-language corpora, and linguistic information at all levels (lexical, syntactic, and semantic).

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