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The PLS (Pronunciation Lexicon) specification is about how to pronounce words and phrases and how to deal with the variability of pronunciations by country, region, person, etc. The Pronunciation Lexicon Specification (PLS) is a W3C Recommendation, which is designed to enable interoperable specification of pronunciation information for both speech recognition and speech synthesis engines within voice browsing applications.

This data is created with the help of detailed experimental study of phonetic and acoustic analysis pertaining to specificities of Assamese Language. This experimental study leads to standardization of Phonemic inventory and modeling for acoustic and phonetic features for each of Indic Languages; a crucial and essential requirement for IPA and W3C PLS, SSML and SGRS standards.

This data contains Acoustics Data, EGG, EPG and Nasometer data of Punjabi Language.

Added on June 7, 2018

0
7

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  • Contributed by : C-DAC Kolkata
  • Product Type : Speech Corpora
  • License Type : Research
  • System Requirement : Not Applicable

The PLS (Pronunciation Lexicon) specification is about how to pronounce words and phrases and how to deal with the variability of pronunciations by country, region, person, etc. The Pronunciation Lexicon Specification (PLS) is a W3C Recommendation, which is designed to enable interoperable specification of pronunciation information for both speech recognition and speech synthesis engines within voice browsing applications.

This data is created with the help of detailed experimental study of phonetic and acoustic analysis pertaining to specificities of Assamese Language. This experimental study leads to standardization of Phonemic inventory and modeling for acoustic and phonetic features for each of Indic Languages; a crucial and essential requirement for IPA and W3C PLS, SSML and SGRS standards.

This data contains Acoustics Data, EGG, EPG and Nasometer data of Manipuri Language.

Added on June 7, 2018

0
2

  More Details
  • Contributed by : C-DAC Kolkata
  • Product Type : Speech Corpora
  • License Type : Research
  • System Requirement : Not Applicable

The PLS (Pronunciation Lexicon) specification is about how to pronounce words and phrases and how to deal with the variability of pronunciations by country, region, person, etc. The Pronunciation Lexicon Specification (PLS) is a W3C Recommendation, which is designed to enable interoperable specification of pronunciation information for both speech recognition and speech synthesis engines within voice browsing applications.

This data is created with the help of detailed experimental study of phonetic and acoustic analysis pertaining to specificities of Assamese Language. This experimental study leads to standardization of Phonemic inventory and modeling for acoustic and phonetic features for each of Indic Languages; a crucial and essential requirement for IPA and W3C PLS, SSML and SGRS standards.

This data contains Acoustics Data, EGG, EPG and Nasometer data of Assamese Language.

Added on June 7, 2018

0
2

  More Details
  • Contributed by : C-DAC Kolkata
  • Product Type : Speech Corpora
  • License Type : Research
  • System Requirement : Not Applicable

Active learning and crowd sourcing are becoming increasingly popular in the machine learning community for fast and cost effective generation of labels for large volumes of data. However, such labels may be noisy. So, it becomes important to ignore the noisy labels for building of a good classifier. We propose a framework for finding the best possible augmentation of a classifier for the character recognition problem using minimum number of crowd labeled samples. The approach inherently rejects the noisy data and tries to accept a subset of correctly labeled data to maximize the classifier performance.

Added on March 27, 2018

144

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  • Contributed by : OCR Consortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Arpit Agarwal,Ritu Garg,Santanu Chaudhury
Author Community Profile :

The paper presents a novel script independent CRF based inferencing framework for character recognition. In this framework we consider a word as a sequence of connected components. The connected components are obtained using different binarization schemes and different possible sequences are considered using a tree structure. CRF uses contextual information to learn perfect primitive sequences and finds the most probable labeling of the sequence of primitives using multiple hypothesis tree to form the correct sequence of alphabets. This approach is particularly suitable for degraded printed document images as it considers multiple alternate hypotheses for correct decision.

Added on March 27, 2018

31

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  • Contributed by : OCR Consortium
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Anupama Ray,Ankit Chandawala,Santanu Chaudhury
Author Community Profile :