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Mixed-memory Markov models for Automatic Language Identification

Automatic language identification (LID) continues to play an integral part in many multilingual speech applications. The most widespread approach to LID is the phonotactic approach, which performs language classification based on the probabilities of phone sequences extracted from the test signal. These probabilities are typically computed using statistical phone n-gram models. In this paper we investigate the approximation of these standard n-gram models by mixed-memory Markov models with application to both a phone-based and an articulatory feature-based LID system. We demonstrate significant improvements in accuracy with a substantially reduced set of parameters on a 10-way language identification task.