Corrections to "Segmental minimum Bayes-risk decoding for automatic speech recognition"

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

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 کامل

Risk 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...

متن کامل

Discriminative training for segmental minimum Bayes risk decoding

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...

متن کامل

Support Vector Machines for Segmental Minimum Bayes Risk Decoding

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...

متن کامل

Ginisupport vector machines for segmental minimum Bayes risk decoding of continuous speech

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...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: IEEE Transactions on Audio, Speech and Language Processing

سال: 2006

ISSN: 1558-7916

DOI: 10.1109/tsa.2005.854087