A Cache-Based Natural Language Model for Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised topic modelling for multi-party spoken discourse
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Linguistic mimicry and trust in text-based CMC
Proceedings of the 2008 ACM conference on Computer supported cooperative work
High frequency word entrainment in spoken dialogue
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Multi-speaker language modeling
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Computational modelling of structural priming in dialogue
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Capturing implicit user influence in online social sharing
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Topic tracking language model for speech recognition
Computer Speech and Language
IEEE Transactions on Multimedia
IEEE Transactions on Audio, Speech, and Language Processing
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In conversations, people tend to mimic their companions' behavior depending on their level of trust. This phenomenon is known as entrainment. We propose a probabilistic model for estimating influences among speakers from conversation data involving multiple people by modeling lexical entrainment. The proposed model estimates word use as a function of the weighted sum of the earlier word use of other speakers. The weights represent influences between speakers. The influences can be efficiently estimated by using the expectation maximization (EM) algorithm. We also develop its online inference procedures for sequentially modeling the dynamics of influence relations. Experiments performed on two meeting data sets one in Japanese and one in English demonstrate the effectiveness of the proposed method.