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{ "item_title" : "Robust Adaptation to Non-Native Accents in Automatic Speech Recognition", "item_author" : [" Silke Goronzy "], "item_description" : "Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems.In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/54/000/325/3540003258_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Robust Adaptation to Non-Native Accents in Automatic Speech Recognition|Silke Goronzy

Robust Adaptation to Non-Native Accents in Automatic Speech Recognition

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Overview

Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems.
In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.

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Details

  • ISBN-13: 9783540003250
  • ISBN-10: 3540003258
  • Publisher: Springer
  • Publish Date: December 2002
  • Dimensions: 9.32 x 6.32 x 0.39 inches
  • Shipping Weight: 0.56 pounds
  • Page Count: 146

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