Multilingual articulatory features

Speech recognition systems based on or aided by articulatory features, such as place and manner of articulation, have been shown to be useful under varying circumstances. Recognizers based on features better compensate channel and noise variability. We show that it is also possible to compensate for inter language variability using articulatory feature detectors. We come to the conclusion that articulatory features can be recognized across languages and that using detectors from many languages can improve the classification accuracy of the feature detectors on a single language. We further demonstrate how those multilingual and cross-lingual detectors can support an HMM based recognizer and thereby significantly reduce the word error rate by up to 12.3% relative. We expect that with the use of multilingual articulatory features it is possible to support the rapid deployment of recognition systems for new target languages.