#21204Second, we show a significant improvement for speaker adaptation (SA) using the new SI corpus and a small amount of speech from thenew ( 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.
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.
measure(ment),12-9-H90-1060,ak
</term>
for
<term>
adaptation
</term>
, the
<term>
error rate
</term>
dropped to 4.1 % --- a 45 % reduction
#21258Using only 40 utterances from the target speaker for adaptation, theerror rate dropped to 4.1% --- a 45% reduction in error compared to the SI result.
other,6-3-H90-1060,ak
. In addition , combination of the
<term>
training speakers
</term>
is done by averaging the statistics
#21100In addition, combination of thetraining speakers is done by averaging the statistics of independently trained models rather than the usual pooling of all the speech data from many speakers prior to training.
other,6-9-H90-1060,ak
40
<term>
utterances
</term>
from the
<term>
target speaker
</term>
for
<term>
adaptation
</term>
, the
<term>
#21252Using only 40 utterances from thetarget speaker for adaptation, the error rate dropped to 4.1% --- a 45% reduction in error compared to the SI result.
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.
measure(ment),14-4-H90-1060,ak
recognition
</term>
, we achieved a 7.5 %
<term>
word error rate
</term>
on a standard
<term>
grammar
</term>
#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.
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.
model,17-3-H90-1060,ak
statistics of independently trained
<term>
models
</term>
rather than the usual
<term>
pooling
#21111In addition, combination of the training speakers is done by averaging the statistics of independently trainedmodels rather than the usual pooling of all the speech data from many speakers prior to training.
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.
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.
lr,26-3-H90-1060,ak
usual
<term>
pooling
</term>
of all the
<term>
speech data
</term>
from many
<term>
speakers
</term>
prior
#21120In addition, combination of the training speakers is done by averaging the statistics of independently trained models rather than the usual pooling of all thespeech data from many speakers prior to training.
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.
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.
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.
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.
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.
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.
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.