Corrections to "Segmental minimum Bayes-risk decoding for automatic speech recognition"
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چکیده
منابع مشابه
Corrections to "Segmental minimum Bayes-risk decoding for automatic speech recognition"
In our recently published paper [1], we presented a risk-based lattice cutting procedure to segment ASR word lattices into smaller sub-lattices as a means to to improve the efficiency of Minimum Bayes-Risk (MBR) rescoring. In the experiments reported [1], some of the hypotheses in the original lattices were inadvertently discarded during segmentation, and this affected MBR performance adversely...
متن کاملRisk based lattice cutting for segmental minimum Bayes-risk decoding
Minimum Bayes Risk (MBR) decoders improve upon MAP decoders by directly optimizing loss function of interest: Word Error Rate MBR decoding is expensive when the search spaces are large Segmental MBR (SMBR) decoding breaks the single utterance-level MBR decoder into a sequence of simpler search problems. – To do this, the N-best lists or lattices need to be segmented We present: A new lattice se...
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A modeling approach is presented that incorporates discriminative training procedures within segmental Minimum Bayes-Risk decoding (SMBR). SMBR is used to segment lattices produced by a general automatic speech recognition (ASR) system into sequences of separate decision problems involving small sets of confusable words. Acoustic models specialized to discriminate between the competing words in...
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Segmental Minimum Bayes Risk (SMBR) Decoding is an approach whereby we use a decoding criterion that is closely matched to the evaluation criterion (Word Error Rate) for speech recognition. This involves the refinement of the search space into manageable confusion sets (ie, smaller sets of confusable words). We propose using Support Vector Machines (SVMs) as a discriminative model in the refine...
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We describe the use of Support Vector Machines (SVMs) for continuous speech recognition by incorporating them in Segmental Minimum Bayes Risk decoding. Lattice cutting is used to convert the Automatic Speech Recognition search space into sequences of smaller recognition problems. SVMs are then trained as discriminative models over each of these problems and used in a rescoring framework. We pos...
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ژورنال
عنوان ژورنال: IEEE Transactions on Audio, Speech and Language Processing
سال: 2006
ISSN: 1558-7916
DOI: 10.1109/tsa.2005.854087