Concordance

Query H90-1060 38 >
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Shuffle 38 (1,771.6 per million)

measure(ment),1-8-H90-1060,bq	the <term> target speaker </term> . Each <term>	reference model	</term> is transformed to the <term> space </term>	#17171 Eachreference model is transformed to the space of the target speaker and combined by averaging.
model,16-3-H90-1060,bq	averaging the <term> statistics > </term> of <term>	independently trained models	</term> rather than the usual pooling of	#17050 In addition, combination of the training speakers is done by averaging the statistics> ofindependently trained models rather than the usual pooling of all the speech data from many speakers prior to training.
measure(ment),14-4-H90-1060,bq	recognition </term> , we achieved a 7.5 % <term>	word error rate	</term> on a standard <term> grammar </term>	#17084 With 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.
measure(ment),12-9-H90-1060,bq	</term> for <term> adaptation </term> , the <term>	error rate	</term> dropped to 4.1 % --- a 45 % reduction	#17199 Using 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,3-4-H90-1060,bq	<term> training </term> . With only 12 <term>	training speakers	</term> for <term> SI recognition </term> , we	#17073 With 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.
tech,8-2-H90-1060,bq	First , we present a new paradigm for <term>	speaker-independent ( SI ) training	</term> of <term> hidden Markov models ( HMM	#16996 First, 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,23-6-H90-1060,bq	corpus </term> and a small amount of <term>	speech	</term> from the new ( target ) <term> speaker	#17142 Second, we show a significant improvement for speaker adaptation (SA) using the new SI corpus and a small amount ofspeech from the new (target) speaker.
tech,15-8-H90-1060,bq	target speaker </term> and combined by <term>	averaging	</term> . Using only 40 <term> utterances </term>	#17185 Each reference model is transformed to the space of the target speaker and combined byaveraging.
lr-prod,26-4-H90-1060,bq	</term> and <term> test set </term> from the <term>	DARPA Resource Management corpus	</term> . This <term> performance </term> is	#17096 With only 12 training speakers for SI recognition, we achieved a 7.5% word error rate on a standard grammar and test set from theDARPA Resource Management corpus.
other,7-8-H90-1060,bq	model </term> is transformed to the <term>	space	</term> of the <term> target speaker </term>	#17177 Each reference model is transformed to thespace of the target speaker and combined by averaging.
other,30-6-H90-1060,bq	speech </term> from the new ( target ) <term>	speaker	</term> . A <term> probabilistic spectral mapping	#17149 Second, we show a significant improvement for speaker adaptation (SA) using the new SI corpus and a small amount of speech from the new (target)speaker.
lr,41-2-H90-1060,bq	traditional practice of using a little <term>	speech	</term> from many <term> speakers </term> . In	#17029 First, 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 littlespeech from many speakers.
other,6-9-H90-1060,bq	40 <term> utterances </term> from the <term>	target speaker	</term> for <term> adaptation </term> , the <term>	#17193 Using 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,13-3-H90-1060,bq	speakers </term> is done by averaging the <term>	statistics >	</term> of <term> independently trained models	#17047 In addition, combination of the training speakers is done by averaging thestatistics > of independently trained models rather than the usual pooling of all the speech data from many speakers prior to training.
other,16-7-H90-1060,bq	reference ) speaker </term> and the <term>	target speaker	</term> . Each <term> reference model </term>	#17167 A probabilistic spectral mapping is estimated independently for each training (reference) speaker and thetarget speaker.
other,15-5-H90-1060,bq	condition for this test suite , using 109 <term>	training speakers	</term> . Second , we show a significant	#17116 This performance is comparable to our best condition for this test suite, using 109training speakers.
tech,8-6-H90-1060,bq	show a significant improvement for <term>	speaker adaptation ( SA )	</term> using the new <term> SI corpus </term>	#17127 Second, 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,7-1-H90-1060,bq	paper reports on two contributions to <term>	large vocabulary continuous speech recognition	</term> . First , we present a new paradigm	#16982 This paper reports on two contributions tolarge vocabulary continuous speech recognition.
other,6-3-H90-1060,bq	. In addition , combination of the <term>	training speakers	</term> is done by averaging the <term> statistics	#17040 In 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.
lr,27-2-H90-1060,bq	</term> , which uses a large amount of <term>	speech	</term> from a few <term> speakers </term> instead	#17015 First, 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.


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