Language Model Adaptation Using Machine-Translated Text for Resource-Deficient Languages
Text corpus size is an important issue when building a language model (LM). This is a particularly important issue for languages where little data is available. This paper introduces an LM adaptation technique to improve an LM built using a small amount of task-dependent text with the help of a mach...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2009-01-01
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| Series: | EURASIP Journal on Audio, Speech, and Music Processing |
| Online Access: | http://dx.doi.org/10.1155/2008/573832 |
| Summary: | Text corpus size is an important issue when building a language model (LM). This is a particularly important issue for languages where little data is available. This paper introduces an LM adaptation technique to improve an LM built using a small amount of task-dependent text with the help of a machine-translated text corpus. Icelandic speech recognition experiments were performed using data, machine translated (MT) from English to Icelandic on a word-by-word and sentence-by-sentence basis. LM interpolation using the baseline LM and an LM built from either word-by-word or sentence-by-sentence translated text reduced the word error rate significantly when manually obtained utterances used as a baseline were very sparse. |
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| ISSN: | 1687-4714 1687-4722 |