Morph-Based Speech Recognition and Modeling of Out-of-Vocabulary Words Across Languages
We explore the use of morph-based language models in large-vocabulary continuous speech recognition systems across four so-called "morphologically rich'' languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. The morphs are subword units discovered in an unsupervised, data-driven way using the em Morfessor algorithm. By estimating n-gram language models over sequences of morphs instead of words, the quality of the language model is improved through better vocabulary coverage and reduced data sparsity. Standard word models suffer from high out-of-vocabulary (OOV) rates, whereas the morph models can recognize previously unseen word forms by concatenating morphs. It is shown that the morph models do perform fairly well on OOVs without compromising the recognition accuracy on in-vocabulary words. The Arabic experiment constitutes the only exception, since here the standard word model outperforms the morph model. Differences in the data sets and the amount of data are discussed as a plausible explanation.