tech,46-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3074
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,bq |
Sources of
<term>
training data
</term>
suitable for
<term>
language modeling
</term>
of
<term>
conversational speech
</term>
are limited .
|
#3023
Sources of training data suitable for language modeling ofconversational speech are limited. |
lr,43-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3071
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,bq |
Sources of
<term>
training data
</term>
suitable for
<term>
language modeling
</term>
of
<term>
conversational speech
</term>
are limited .
|
#3020
Sources of training data suitable forlanguage modeling of conversational speech are limited. |
other,49-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3077
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 using class-dependent interpolation ofN-grams. |
tech,27-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#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 the targetrecognition task, but also that it is possible to get bigger performance gains from the data by using class-dependent interpolation of N-grams. |
other,21-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3049
In this paper, we show how training data can be supplemented with text from the web filtered to match thestyle 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. |
other,13-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3041
In this paper, we show how training data can be supplemented withtext 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. |
other,23-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3051
In this paper, we show how training data can be supplemented with text from the web filtered to match the style and/ortopic 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. |
lr,2-1-N03-2003,bq |
Sources of
<term>
training data
</term>
suitable for
<term>
language modeling
</term>
of
<term>
conversational speech
</term>
are limited .
|
#3016
Sources oftraining data suitable for language modeling of conversational speech are limited. |
lr,7-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3035
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. |
other,16-2-N03-2003,bq |
In this paper , we show how
<term>
training data
</term>
can be supplemented with
<term>
text
</term>
from the
<term>
web
</term>
filtered to match the
<term>
style
</term>
and/or
<term>
topic
</term>
of the target
<term>
recognition task
</term>
, but also that it is possible to get bigger performance gains from the
<term>
data
</term>
by using
<term>
class-dependent interpolation
</term>
of
<term>
N-grams
</term>
.
|
#3044
In this paper, we show how training data can be supplemented with text from theweb 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. |