Parameter Estimation of Statistical Models Using Convex Optimization

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Deep Learning: Methods and Applications (2014, background): “Many of the discriminative techniques for supervised learning in signal and information processing are shallow architectures such as HMMs [52, 127, 147, 186, 188, 290, 394, 418] and conditional random fields (CRFs) [151, 155, 281, 400, 429, 446].”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, methodology): “Recent surveys have provided detailed analysis on and comparisons among the various popular techniques within this specific paradigm pertaining to HMM-like generative models, as well as a unified treatment of these techniques [74], [114], [119], [120].”
An Overview of Deep-Structured Learning for Information Processing (2011, background): “Many of the discriminative techniques in signal and information processing apply to shallow architectures such as HMMs (e.g., He et al., 2008; Jiang and Li, 2010; Xiao and Deng, 2010; Gibson and Hain, 2010) or CRFs (e.g., Yang and Furui, 2009; Yu, Li, and Deng, 2010; Hifny and Renals, 2009).”