| 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. |
| 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. |
| tech,14-2-H90-1060,bq | speaker-independent ( SI ) training </term> of <term> | hidden Markov models ( HMM ) | </term> , which uses a large amount of <term> | #17002 First, 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,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. |
| other,31-2-H90-1060,bq | amount of <term> speech </term> from a few <term> | speakers | </term> instead of the traditional practice | #17019 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 fewspeakers instead of the traditional practice of using a little speech from many speakers. |
| 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,44-2-H90-1060,bq | little <term> speech </term> from many <term> | speakers | </term> . In addition , combination of the | #17032 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 little speech from manyspeakers. |
| 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. |
| 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. |
| 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. |
| lr,27-3-H90-1060,bq | than the usual pooling of all the <term> | speech data | </term> from many <term> speakers </term> prior | #17061 In 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. |
| other,31-3-H90-1060,bq | the <term> speech data </term> from many <term> | speakers | </term> prior to <term> training </term> . With | #17065 In 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. |
| tech,34-3-H90-1060,bq | many <term> speakers </term> prior to <term> | training | </term> . With only 12 <term> training speakers | #17068 In 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 many speakers prior totraining. |
| 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,6-4-H90-1060,bq | 12 <term> training speakers </term> for <term> | SI recognition | </term> , we achieved a 7.5 % <term> word error | #17076 With 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. |
| 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. |
| lr,20-4-H90-1060,bq | word error rate </term> on a standard <term> | grammar | </term> and <term> test set </term> from the <term> | #17090 With 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. |
| lr,22-4-H90-1060,bq | a standard <term> grammar </term> and <term> | test set | </term> from the <term> DARPA Resource Management | #17092 With 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. |
| 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. |
| measure(ment),1-5-H90-1060,bq | Resource Management corpus </term> . This <term> | performance | </term> is comparable to our best condition | #17102 Thisperformance is comparable to our best condition for this test suite, using 109 training speakers. |
