This paper concerns the development of statistical language models of the Slovenian language for use in an automatic speech recognition system. The proposed techniques are language-independent and can be applied to other highly inflected Slavic languages. The large number of unique words in inflected languages is identified as the primary reason for performance degradation. This article discusses the concept of word-formation in the Slovenian language, which is also common to all Slavic languages. The main problems are outlined for word-based language models. A novel variation on the N-gram modelling theme is examined where, instead of using words, modelling units are chosen to be stems and endings. Only data-driven algorithms are employed, which decompose words automatically. A significant reduction in the OOV rate results when using stems and endings for modelling the Slovenian language. The final part of this article focuses on building a speech recogniser. Two different decoding strategies have been employed: one-pass and two-pass search decoders. Language modelling experiments have been performed using the VECER newswire text corpus, and recognition experiments have been conducted using the SNABI Slovenian speech database. The new language model resulted in the reduction of the OOV rate by 64%, and the recognition accuracy was improved by 4.34%.
['Mirjam Sepesy Maucec', 'Tomaz Rotovnik', 'Melita Zemljak']
Modelling Highly Inflected Slovenian Language