Abstract
Norwegian is a challenging language for automatic speech recognition research because it has two written standards (Bokmål and Nynorsk) and a large number of distinct dialects, from which none has status of an official spoken norm. A traditional lexicon-based approach to ASR leads to a huge lexicon (because of the two standards and also due to compound words) with many spelling and pronunciation variants, and consequently to a large (and sparse) language model (LM). We have built a system with 601k-word lexicon and an acoustic model (AM) based on several types of neural networks and compare its performance with a lexicon-free end-to-end system developed in the ESPnet framework. For evaluation we use a publically available dataset of Norwegian parliament speeches that offers 100 h for training and 12 h for testing. In spite of this rather limited training resource, the lexicon-free approach yields significantly better results (13.0% word-error rate) compared to the best system with the lexicon, LM and neural network AM (that achieved 22.5% WER).
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Acknowledgements
This work was supported by the Technology Agency of the Czech Republic (project No. TO01000027).
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Nouza, J., Červa, P., Žd’ánský, J. (2022). Lexicon-based vs. Lexicon-free ASR for Norwegian Parliament Speech Transcription. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2022. Lecture Notes in Computer Science(), vol 13502. Springer, Cham. https://doi.org/10.1007/978-3-031-16270-1_33
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