Language recognition with discriminative keyword selection

One commonly used approach for language recognition is to convert the input speech into a sequence of tokens such as words or phones and then to use these token sequences to determine the target language. The language classification is typically performed by extracting N-gram statistics from the token sequences and then using an N-gram language model or support vector machine (SVM) to perform the classification. One problem with these approaches is that the number of N-grams grows exponentially as the order N is increased. This is especially problematic for an SVM classifier as each utterance is represented as a distinct N-gram vector. In this paper we propose a novel approach for modeling higher order N-grams using an SVM via an alternating filter-wrapper feature selection method. We demonstrate the effectiveness of this technique on the NIST 2007 language recognition task.