Robust Adaptation to Non-Native Accents in Automatic Speech Recognition

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.

• Automat
• Automatic Speech Recognition
• Confidence Measures
• Human-Machine Interfaces
• MLLR
• Natural Language Processing
• Non-Native Accents
• Phone Duration
• Pronunciation Modeling
• Speaker Adaptation
• Speech Processing
• algorithms
• cognition
• modeling
• speech recognition

• SCLE, MMI Lab, Sony International (Europe) GmbH, Stuttgart, Germany

  Silke Goronzy

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