Attention, intentions, and the structure of discourse
Computational Linguistics
Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Coupling CCG and hybrid logic dependency semantics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
A salience driven approach to robust input interpretation in multimodal conversational systems
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Signal Processing - Special section: Multimodal human-computer interfaces
Incremental natural language processing for HRI
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Spoken language processing: Piecing together the puzzle
Speech Communication
Crossmodal content binding in information-processing architectures
Proceedings of the 3rd ACM/IEEE international conference on Human robot interaction
Towards an integrated robot with multiple cognitive functions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Semiotic schemas: A framework for grounding language in action and perception
Artificial Intelligence - Special volume on connecting language to the world
A salience-driven approach to speech recognition for human-robot interaction
ESSLLI'08/09 Proceedings of the 2008 international conference on Interfaces: explorations in logic, language and computation
High efficiency realization for a wide-coverage unification grammar
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
A salience-driven approach to speech recognition for human-robot interaction
ESSLLI'08/09 Proceedings of the 2008 international conference on Interfaces: explorations in logic, language and computation
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We present an implemented model for speech recognition in natural environments which relies on contextual information about salient entities to prime utterance recognition. The hypothesis underlying our approach is that, in situated human-robot interaction, speech recognition performance can be significantly enhanced by exploiting knowledge about the immediate physical environment and the dialogue history. To this end, visual salience (objects perceived in the physical scene) and linguistic salience (previously referred-to objects within the current dialogue) are integrated into a single cross-modal salience model. The model is dynamically updated as the environment evolves, and is used to establish expectations about uttered words which are most likely to be heard given the context. The update is realised by continously adapting the word-class probabilities specified in the statistical language model. The present article discusses the motivations behind our approach, describes our implementation as part of a distributed, cognitive architecture for mobile robots, and reports the evaluation results on a test suite.