Theoretical error prediction for a language identification system using optimal phoneme clustering

using Optimal Phoneme Clustering Kay M. Berkling, Etienne Barnard (berkling,barnard)@cse.ogi.edu Center for Spoken Language Understanding, Oregon Graduate Institute of Science and Technology Abstract A neural network based language identi cation system is described, which uses language independent phoneme clusters as speech units to recognize the language spoken by native speakers over the telephone. We extend our previous work comparing phoneme-cluster and phoneme based approaches to language identi cation [1]. By creating a new speech unit valid across all languages in a theoreticallymotivatedmanner, we circumvent problems that are associated with ne phonemic modelling such as high complexity [4], extensive training requirements [2], and the linguistically arbitrary reduction to subsets of phonemes [4]. A common set of speech units across languages allows us to automatically derive discriminating sequences of any length and theoretically estimate the language identi cation error. We demonstrate our implemented system for German vs. English on the OGI-TS database.