Spoken language identification using a genetic-based fusion approach to combine acoustic and universal phonetic results

Abstract

Identification of the spoken languages in an audio file is performed automatically using the spoken language identification (LID) process. In this paper, we proposed a genetic-based fusion method to combine the score probabilities of an x-vector-based acoustic LID (ALID) and a phonetic LID (PLID) system. The ALID system is based on an LDA classifier able to identify different languages using x-vectors, while the PLID system is based on an SVM classifier which takes into account perplexities as its feature vector, which are derived from phone language models utilizing a universal phone recognizer named Allosaurus. With the help of genetic-based fusion, 54 weights will be extracted. Having 27 languages in our database and two different LID systems results in 54 weights for our fusion. The individual results of our acoustic and phonetic LID systems are eventually combined by applying these weights. Based on the experimental results on 27 languages from the NIST-LRE09 database, the fusion of the acoustic system and the phonetic system results in 93.30% accuracy, which has approximately a 21% reduction in identification error to our best baseline system with 91.50% accuracy.