Large vocabulary continuous speech recognition of an inflected language using stems and endings

Abstract

In this article, we focus on creating a large vocabulary speech recognition system for the Slovenian language. Currently, state-of-the-art recognition systems are able to use vocabularies with sizes of 20,000 to 100,000 words. These systems have mostly been developed for English, which belongs to a group of uninflectional languages. Slovenian, as a Slavic language, belongs to a group of inflectional languages. Its rich morphology presents a major problem in large vocabulary speech recognition. Compared to English, the Slovenian language requires a vocabulary approximately 10 times greater for the same degree of text coverage. Consequently, the difference in vocabulary size causes a high degree of OOV (out-of-vocabulary words). Therefore OOV words have a direct impact on recognizer efficiency. The characteristics of inflectional languages have been considered when developing a new search algorithm with a method for restricting the correct order of sub-word units, and to use separate language models based on sub-words. This search algorithm combines the properties of sub-word-based models (reduced OOV) and word-based models (the length of context). The algorithm also enables better search-space limitation for sub-word models. Using sub-word models, we increase recognizer accuracy and achieve a comparable search space to that of a standard word-based recognizer. Our methods were evaluated in experiments on a SNABI speech database.