N10-1024 fricative or sonorant ) required for acoustic model training . 8 Conclusion Unlike previous
N10-1024 the transcriptions for English acoustic model training . Every single misspelling or
P07-1012 from the database was used for acoustic model training , of which less than half was
H01-1059 approach for lightly supervised acoustic model training , we describe our standard training
H01-1059 lightly supervised techniques for acoustic model training . The strategy taken is to use
H01-1059 and the use of low cost data for acoustic model training . We have explored the genericity
D12-1070 pause , and noise . During the acoustic model training , tied-state cross-word triphones
H01-1059 supervision for lightly supervised acoustic model training . Table 5 : Word error rates
H01-1059 algorithm ) 6 . Run the standard acoustic model training procedure on the speech segments
N07-2023 talk - ers . However , following acoustic model training and use ( q * ) , the VAD error
D14-1156 2002 was used to bootstrap the acoustic model training . The vocabulary size is about
W04-1612 diacritizing Arabic text for use in acoustic model training for ASR . A comparison of the
E14-1065 discriminative training applied . The acoustic model training data is 186h of Broadcast News-style
W06-1646 et al. , 2003 ) . 6.1 Task For acoustic model training , transcripts are available for
H93-1022 for the two applications ( e.g. acoustic model training , model topology and language
H01-1059 techniques for lightly supervised acoustic model training ; and exploring transparent methods
H01-1059 manually transcribed data for acoustic model training and large normalized text corpora
H01-1059 procedure in the next section . 4 . ACOUSTIC MODEL TRAINING HMM training requires an alignment
W14-2204 in § 5.4 ( a precursor for acoustic model training in Speech Recognition systems
P13-1005 trained on the 1996 and 1997 Hub4 acoustic model training sets ( about 150 hours of data
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