Combining missing-feature theory, speech enhancement, and speaker-dependent/-independent modeling for speech separation

By Ji Ming, Timothy J. Hazen, James R. Glass. 2006

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Citations in the corpus

Learning Domain-Independent Dialogue Policies via Ontology Parameterisation (2015, methodology): “|{ v There are also a number of slot-independent features applied to all action types, including the belief over communication methods (Thomson and Young, 2010) and the marginal conﬁdence scores of user dialogue act types in the current turn.”
A Simple and Generic Belief Tracking Mechanism for the Dialog State Tracking Challenge: On the believability of observed information (2013, methodology): “Such methods are usually developed under a partially observable Markov decision process (POMDP) framework (Young et al., 2010; Thomson and Young, 2010; Williams, 2010), where the distribution over dialogue states is called a ‘belief’ and is modelled as a posterior updated every turn given an observa”
Bat algorithm: a novel approach for global engineering optimization (2012, methodology): “Since then, this problem has also been solved using several other methods such as GA (Gen and Cheng 1997, Homaifar et al. 1994), harmony search (HS) algorithm (Lee and Geem 2004, Fesanghary et al. 2008), and PSO (He et al. 2004, Shi and Eberhart 1998).”
Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering (2019, methodology): “Early models of dialogue state tracking (Thomson & Young, 2010; Wang & Lemon, 2013; Henderson et al., 2014) rely on handcrafted features to extract utterance semantics, and then use these features to predict dialogue states.”
The Dialog State Tracking Challenge (2013, background): “In end-to-end dialog systems, dialog state tracking has been shown to improve overall system performance (Young et al., 2010; Thomson and Young, 2010).”
Policy committee for adaptation in multi-domain spoken dialogue systems (2015, methodology): “Here we use the Bayesian Update of Dialogue State (BUDS) dialogue model [17].”
POMDP-based dialogue manager adaptation to extended domains (2013, background): “This provides increased robustness to errors in speech understanding and automatic dialogue policy optimisation via reinforcement learning (Roy et al., 2000; Zhang et al., 2001; Williams and Young, 2007; Young et al., 2010; Thomson and Young, 2010).”
Multi-task Learning for Joint Language Understanding and Dialogue State Tracking (2018, background): “ing models have ranged from rule-based (Wang and Lemon, 2013), to generative (Thomson and Young, 2010), discriminative (Henderson et al.”
Dialog state tracking, a machine reading approach using Memory Network (2016, methodology): “Other approaches to cope with the scalability problem in dialog state tracking is to adopt a factored dynamic Bayesian network by making conditional independence assumptions among dialog state components, and then using approximate inference algorithms such as loopy belief propagation [24]”