#21143With only 12 training speakers for SI recognition, we achieved a 7.5%word error rate on a standard grammar and test set from the DARPA Resource Management corpus.
other,15-5-H90-1060,ak
<term>
test suite
</term>
, using 109
<term>
training speakers
</term>
. Second , we show a significant
#21175This performance is comparable to our best condition for this test suite, using 109training speakers.
other,3-4-H90-1060,ak
<term>
training
</term>
. With only 12
<term>
training speakers
</term>
for
<term>
SI recognition
</term>
, we
#21132With only 12training speakers for SI recognition, we achieved a 7.5% word error rate on a standard grammar and test set from the DARPA Resource Management corpus.
other,3-9-H90-1060,ak
combined by averaging . Using only 40
<term>
utterances
</term>
from the
<term>
target speaker
</term>
#21249Using only 40utterances from the target speaker for adaptation, the error rate dropped to 4.1% --- a 45% reduction in error compared to the SI result.
model,1-7-H90-1060,ak
<term>
new ( target ) speaker
</term>
. A
<term>
probabilistic spectral mapping
</term>
is estimated independently for each
#21211Aprobabilistic spectral mapping is estimated independently for each training (reference) speaker and the target speaker.
other,22-4-H90-1060,ak
a standard
<term>
grammar
</term>
and
<term>
test set
</term>
from the
<term>
DARPA Resource Management
#21151With only 12 training speakers for SI recognition, we achieved a 7.5% word error rate on a standard grammar andtest set from the DARPA Resource Management corpus.
model,1-8-H90-1060,ak
the
<term>
target speaker
</term>
. Each
<term>
reference model
</term>
is transformed to the space of the
#21230Eachreference model is transformed to the space of the target speaker and combined by averaging.
other,9-7-H90-1060,ak
is estimated independently for each
<term>
training ( reference ) speaker
</term>
and the
<term>
target speaker
</term>
#21219A probabilistic spectral mapping is estimated independently for eachtraining ( reference ) speaker and the target speaker.
other,31-2-H90-1060,ak
amount of
<term>
speech
</term>
from a few
<term>
speakers
</term>
instead of the traditional practice
#21079First, we present a new paradigm for speaker-independent (SI) training of hidden Markov models (HMM), which uses a large amount of speech from a fewspeakers instead of the traditional practice of using a little speech from many speakers.
tech,8-6-H90-1060,ak
show a significant improvement for
<term>
speaker adaptation ( SA )
</term>
using the new
<term>
SI corpus
</term>
#21186Second, we show a significant improvement forspeaker adaptation ( SA ) using the new SI corpus and a small amount of speech from the new (target) speaker.
tech,8-2-H90-1060,ak
First , we present a new paradigm for
<term>
speaker-independent ( SI ) training
</term>
of
<term>
hidden Markov models ( HMM
#21056First, we present a new paradigm forspeaker-independent ( SI ) training of hidden Markov models (HMM), which uses a large amount of speech from a few speakers instead of the traditional practice of using a little speech from many speakers.
tech,9-9-H90-1060,ak
the
<term>
target speaker
</term>
for
<term>
adaptation
</term>
, the
<term>
error rate
</term>
dropped
#21255Using only 40 utterances from the target speaker foradaptation, the error rate dropped to 4.1% --- a 45% reduction in error compared to the SI result.
tech,6-4-H90-1060,ak
12
<term>
training speakers
</term>
for
<term>
SI recognition
</term>
, we achieved a 7.5 %
<term>
word error
#21135With only 12 training speakers forSI recognition, we achieved a 7.5% word error rate on a standard grammar and test set from the DARPA Resource Management corpus.
other,30-3-H90-1060,ak
the
<term>
speech data
</term>
from many
<term>
speakers
</term>
prior to
<term>
training
</term>
. With
#21124In addition, combination of the training speakers is done by averaging the statistics of independently trained models rather than the usual pooling of all the speech data from manyspeakers prior to training.
other,44-2-H90-1060,ak
of using a little speech from many
<term>
speakers
</term>
. In addition , combination of the
#21092First, we present a new paradigm for speaker-independent (SI) training of hidden Markov models (HMM), which uses a large amount of speech from a few speakers instead of the traditional practice of using a little speech from manyspeakers.
lr,16-6-H90-1060,ak
adaptation ( SA )
</term>
using the new
<term>
SI corpus
</term>
and a small amount of
<term>
speech
#21194Second, we show a significant improvement for speaker adaptation (SA) using the newSI corpus and a small amount of speech from the new (target) speaker.
lr,27-2-H90-1060,ak
</term>
, which uses a large amount of
<term>
speech
</term>
from a few
<term>
speakers
</term>
instead
#21075First, we present a new paradigm for speaker-independent (SI) training of hidden Markov models (HMM), which uses a large amount ofspeech from a few speakers instead of the traditional practice of using a little speech from many speakers.
lr,23-6-H90-1060,ak
corpus
</term>
and a small amount of
<term>
speech
</term>
from the
<term>
new ( target ) speaker
#21201Second, we show a significant improvement for speaker adaptation (SA) using the new SI corpus and a small amount ofspeech from the new (target) speaker.
model,14-2-H90-1060,ak
speaker-independent ( SI ) training
</term>
of
<term>
hidden Markov models ( HMM )
</term>
, which uses a large amount of
<term>
#21062First, we present a new paradigm for speaker-independent (SI) training ofhidden Markov models ( HMM ), which uses a large amount of speech from a few speakers instead of the traditional practice of using a little speech from many speakers.
lr,20-4-H90-1060,ak
word error rate
</term>
on a standard
<term>
grammar
</term>
and
<term>
test set
</term>
from the
<term>
#21149With only 12 training speakers for SI recognition, we achieved a 7.5% word error rate on a standardgrammar and test set from the DARPA Resource Management corpus.