Combining Weak Tokenisers for Phonotactic Language Recognition in a Resource-Constrained Setting

Abstract

In the phonotactic approach for language recognition, a phone tokeniser is normally used to transform the audio signal into acoustic tokens. The language identity of the speech is modelled by the occurrence statistics of the decoded tokens. The performance of this approach depends heavily on the quality of the audio tokeniser. A high-quality tokeniser in matched condition is not always available for a language recognition task. This study investigated into the performance of a phonotactic language recogniser in a resource-constrained setting, following NIST LRE 2015 specification. An ensemble of phone tokenisers was constructed by applying unsupervised sequence training on different target languages followed by a score-based fusion. This method gave 5−7% relative performance improvement to baseline system on LRE 2015 eval set. This gain was retained when the ensemble phonotactic system was further fused with an acoustic iVector system