A stochastic model of speech incorporating hierarchical nonstationarity

By Li Deng. 1993

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Citations in the corpus

Deep Learning: Methods and Applications (2014, background): “Specifically, the early work described in [71, 72, 83, 84, 99, 274] generalized and extended the conventional shallow and conditionally independent HMM structure by imposing dynamic constraints, in the form of polynomial trajectory, on the HMM parameters.”
From HMM's to segment models: a unified view of stochastic modeling for speech recognition (1996, background): “Deng extended his parametric trajectory model [15] to the Gauss-Markov case [61], but did not assess its performance in recognition experiments.”
Variational Learning for Switching State-Space Models (2000, background): “…the linear dynamics of SSMs has developed in fields ranging from econometrics to control engineering (Harrison & Stevens, 1976; Chang & Athans, 1978; Hamilton, 1989; Shumway & Stoffer, 1991; Bar-Shalom & Li, 1993; Deng, 1993; Kadirkamanathan & Kadirkamanathan, 1996; Chaer, Bishop, & Ghosh, 1997).”
An Overview of Deep-Structured Learning for Information Processing (2011, unknown): “Specifically, the early work described in (Deng 1992, 1993; Deng et al., 1994; Ostendorf et al., 1996, Deng and Sameti, 1996) generalized and extended the conventional shallow HMM structure by imposing dynamic constraints, in the form of polynomial trajectory, on the HMM parameters.”
A dynamic, feature-based approach to the interface between phonology and phonetics for speech modeling and recognition (1998, unknown): “can be found in [15,17,31].”