Adaptation of Morpheme-based Speech Recognition for Foreign Entity Names
Mansikkaniemi Andr eacute, Kurimo Mikko · Frontiers in artificial intelligence and applications · 2012
In this work we study a set of adaptation methods for improving the recognition accuracy of foreign entity names in morph-based speech recognition for Finnish. Supervised forms of language model and lexicon adaptation are evaluated. Morpheme adaptation is performed by restoring over-segmented foreign words back into their dictionary forms. This is important for determining the correct pronunciation. To further improve pronunciation modeling of foreign words, non-native phonemes are included in the acoustic model by augmenting the training set with English sentences spoken by native Finnish speakers. English phonemes which don't have any close native counterpart are included into the Finnish phoneme set. A combination of language model, acoustic model, pronunciation and morpheme adaptation produces the lowest error rate for foreign entity names. We also performed tests to determine whether improved recognition of foreign words improves performance of a spoken document retrieval task. We were unable to verify any significant improvements. However, the test queries in this particular material included few foreign words, so a big performance improvement wasn't expected.