Abstrakti
We study class n-gram models for very large vocabulary speech recognition of Finnish and Estonian. The models are trained with vocabulary sizes of several millions of words using automatically derived classes. To evaluate the models on Finnish and an Estonian broadcast news speech recognition task, we modify Aalto University’s LVCSR decoder to operate with the class n-grams and very large vocabularies. Linear interpolation of a standard n-gram model and a class n-gram model provides relative perplexity improvements of 21.3% for Finnish and 12.8% for Estonian over the n-gram model. The relative improvements in word error rates are 5.5% for Finnish and 7.4% for Estonian. We also compare our word-based models to a state-of-the-art unlimited vocabulary recognizer utilizing subword n-gram models, and show that the very large vocabulary word-based models can perform equally well or better.
Alkuperäiskieli | Englanti |
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Otsikko | Proceedings of the 4th International Conference on Statistical Language and Speech Processing, SLSP 2016 |
Kustantaja | Springer |
Sivut | 133-144 |
Sivumäärä | 12 |
Vuosikerta | 9918 LNCS |
ISBN (painettu) | 9783319459240, 9783319459257 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 2016 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisussa |
Tapahtuma | International Conference on Statistical Language and Speech Processing - Pilsen, Tshekki Kesto: 11 lokak. 2016 → 12 lokak. 2016 Konferenssinumero: 4 http://grammars.grlmc.com/SLSP2016/ |
Julkaisusarja
Nimi | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Vuosikerta | 9918 LNCS |
ISSN (painettu) | 03029743 |
ISSN (elektroninen) | 16113349 |
Conference
Conference | International Conference on Statistical Language and Speech Processing |
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Lyhennettä | SLSP |
Maa/Alue | Tshekki |
Kaupunki | Pilsen |
Ajanjakso | 11/10/2016 → 12/10/2016 |
www-osoite |