A recognition method with parametric trajectory synthesized using direct relations between static and dynamic feature vector time series

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Deep Learning: Methods and Applications (2014, background): “Subsequent work added a new hidden layer into the dynamic model to explicitly account for the target-directed, articulatory-like properties in human speech generation [45, 73, 74, 83, 96, 75, 90, 231, 232, 233, 251, 282].”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “Subsequent work added a new hidden layer into the dynamic model so as to explicitly account for the target-directed, articulatory-like properties in human speech generation (Deng and Ramsay, 1997; Bridle et al., 1998; Deng, 1999; Picone et al., 1999; Deng, 2003; Minami et al., 2002; Deng and Huang, ”
Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences (2004, methodology): “While we derived the speech parameter generation algorithm in order to construct HMM-based speech synthesizers [18] which can synthesize speech with various voice characteristics, the generation algorithm was also applied to speech recognition in [19].”