Random walk theory applied to language identification

We discuss the evaluation of the RPI language identification system by the National Institute of Standards and Technologies (NIST). This system is based on an acousto-phonetic approach where the phonemes present in a language are identified by a hidden semi-Markov model (HSMM). The HSMM was also developed at RPI. Knowledge of these phonemes provides us with the necessary probabilistic framework for classifier design. The classifier used in this system is designed in such a way that language specific scores generated during an evaluation form a random walk. Random walk theory has extensive applications in ecology, metallurgy, chemistry and physics. Until recently random walk theory has been primarily used as a tool for the measurement of the territory covered by a diffusing particle. We now show that random walk theory can be used to effectively design a language identification system