Double bigram-decoding in phonotactic language identification

In this paper a phonotactic language identification system that employs a multilingual phone-recognizer with multiple language-dependent grammars to tokenize the spoken signal into several phone-streams is described. For each stream an independent set of language models is used to compute the language scores that are subsequently processed by two classification stages. Thus, the system acquires information from both the original-label and the decoded-phone statistics. A discriminative weighting method is applied in the second stage for better distinguishing between similar languages. A modified language-bigram model, the so-called skip-gram, that allows exploiting of a wider phonotactic context without increasing the estimation costs of a standard bigram, is introduced. Measured on the NIST´95 evaluation set, the described system outperforms the state-of-the-art phonotactic components that use multiple recognizers, and is, at the same time, less computationally expensive.