Large vocabulary continuous speech recognition of Broadcast News - The Philips/RWTH approach
Speech Communication - Special issue on automatic transcription of broadcast news data
Large Vocabulary Speech Recognition of Slovenian Language Using Data-Driven Morphological Models
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Modeling filled pauses for spontaneous speech recognition applications
AEE'08 Proceedings of the 7th WSEAS International Conference on Application of Electrical Engineering
Syllable Based Language Model for Large Vocabulary Continuous Speech Recognition of Polish
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Slovenian spontaneous speech recognition and acoustic modeling of filled pauses and onomatopoeas
WSEAS Transactions on Signal Processing
Morpheme-Based Automatic Speech Recognition of Basque
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
COST'09 Proceedings of the Second international conference on Development of Multimodal Interfaces: active Listening and Synchrony
Automatic speech recognition for under-resourced languages: A survey
Speech Communication
Language Resources and Evaluation
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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.