| tech,46-2-N03-2003,ak | from the <term> data </term> by using <term> | class-dependent interpolation | </term> of <term> N-grams </term> . In order | #3075 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by usingclass-dependent interpolation of N-grams. | |
| other,9-1-N03-2003,ak | for <term> language modeling </term> of <term> | conversational speech | </term> are limited . In this paper , we | #3024 Sources of training data suitable for language modeling ofconversational speech are limited. | |
| lr,43-2-N03-2003,ak | bigger performance gains from the <term> | data | </term> by using <term> class-dependent interpolation | #3072 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from thedata by using class-dependent interpolation of N-grams. | |
| tech,6-1-N03-2003,ak | <term> training data </term> suitable for <term> | language modeling | </term> of <term> conversational speech </term> | #3021 Sources of training data suitable forlanguage modeling of conversational speech are limited. | |
| tech,26-2-N03-2003,ak | </term> and/or <term> topic </term> of the <term> | target recognition task | </term> , but also that it is possible to | #3055 In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/or topic of thetarget recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. | |
| lr,2-1-N03-2003,ak | tagging result </term> . Sources of <term> | training data | </term> suitable for <term> language modeling | #3017 Sources oftraining data suitable for language modeling of conversational speech are limited. | |
| lr,7-2-N03-2003,ak | limited . In this paper , we show how <term> | training data | </term> can be supplemented with <term> text | #3036 In this paper, we show howtraining data can be supplemented with text from the web filtered to match the style and/or topic of the target recognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. |
