PCA-based Feature Extraction for Phonotactic Language Recognition

摘要

Phonotactic language recognition is one of major techniques used for automatic recognition of spoken languages. We propose a feature extraction technique based on PCA to be used with SVM-based systems. This technique improves speed of the training, in some cases more than 1000 times, allowing systems to be effectively trained on much larger data sets. Speed-up of the test phase can be even greater, which makes the resulting systems much more useful for processing large amounts of data. We report our results on NIST LRE 2009 task.