An error-corrective language-model adaptation for automatic speech recognition

We present a new language model adaptation framework integrated with error handling method to improve accuracy of speech recognition and performance of spoken language applications. The proposed error corrective language model adaptation approach exploits domain-specific language variations and recognition environment characteristics to provide robustness and adaptability for a spoken language system. We demonstrate some experiments of spoken dialogue tasks and empirical results which show an improvement of the accuracy for both speech recognition and spoken language understanding.