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Description
  • The iVector is a low-dimensional fixed-length representation of information about speaker and acoustic environment. To utilize iVectors for adaptation, region dependent linear transforms (RDLT) are discriminatively trained using the MPE criterion on large amounts of annotated data to extract the relevant information from iVectors and to compensate speech features. The approach was tested on standard CTS data. We found it to be complementary to common adaptation techniques. On a well-tuned RDLT system with standard CMLLR adaptation we reached an 0.8% additive absolute WER improvement.
  • The iVector is a low-dimensional fixed-length representation of information about speaker and acoustic environment. To utilize iVectors for adaptation, region dependent linear transforms (RDLT) are discriminatively trained using the MPE criterion on large amounts of annotated data to extract the relevant information from iVectors and to compensate speech features. The approach was tested on standard CTS data. We found it to be complementary to common adaptation techniques. On a well-tuned RDLT system with standard CMLLR adaptation we reached an 0.8% additive absolute WER improvement. (en)
Title
  • iVector-Based Discriminative Adaptation for Automatic Speech Recognition
  • iVector-Based Discriminative Adaptation for Automatic Speech Recognition (en)
skos:prefLabel
  • iVector-Based Discriminative Adaptation for Automatic Speech Recognition
  • iVector-Based Discriminative Adaptation for Automatic Speech Recognition (en)
skos:notation
  • RIV/00216305:26230/11:PU96155!RIV12-TA0-26230___
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  • P(FR-TI1/034), P(GA102/08/0707), P(TA01011328), Z(MSM0021630528)
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  • 206001
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  • RIV/00216305:26230/11:PU96155
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  • Automatic speech recognition, I-vector, Discriminative adaptation (en)
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  • [869AD6C186B0]
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  • Hilton Waikoloa Village Resort, Big Island, Hawa
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  • Hilton Waikoloa Village, Big Island, Hawaii
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  • Proceedings of ASRU 2011
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  • Burget, Lukáš
  • Karafiát, Martin
  • Černocký, Jan
  • Glembek, Ondřej
  • Matějka, Pavel
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number of pages
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  • IEEE Signal Processing Society
https://schema.org/isbn
  • 978-1-4673-0366-8
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  • 26230
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