An investigation of segmental hidden dynamic models of speech coarticulation for automatic speech recognition

By John Scott Bridle. 1998

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Citations in the corpus

Deep Learning: Methods and Applications (2014, background): “It is important to note that the unique elasticity of temporal dynamics of speech as elaborated in [45, 73, 76, 83] would require temporally”
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, ”
Efficient decoding strategies for conversational speech recognition using a constrained nonlinear state-space model (2003, methodology): “One HMM system was also trained on the half an hour data, we call it HMM-baseline system [5].”
A bidirectional target-filtering model of speech coarticulation and reduction: two-stage implementation for phonetic recognition (2006, unknown): “HE IMPORTANCE of incorporating structures of human speech and language into statistical models for technology applications has been well known, and active research in this direction has been pursued in recent years [2], [3], [4], [6], [10], [11], [19], [27], [31], [33].”
Target-directed mixture dynamic models for spontaneous speech recognition (2004, background): “During our earlier work [4], [10], we found that the nonlinear function represented by the MLP had not provided an accurate nonlinear mapping relationship between the VTR and the MFCC spaces.”
Structured speech modeling (2006, methodology): “Various simplified implementations of the aforementioned generic nonlinear system model have appeared in the literature (e.g., [4], [15], [17], [20], [24], [26], [37]).”
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition (2012, background): “The weaknesses in how HMMs deal with the temporal dimen-sion of speech inputs have been analyzed in detail in [78]–[81].”
Dynamic Speech Models: Theory, Algorithms, and Applications (2006, methodology): “A GENERAL MODELING AND COMPUTATIONAL FRAMEWORK 11 empirical methods for modeling pseudo-articulatory dynamics or abstract hidden dynamics for the purpose of speech recognition can be found in [42, 44–46, 58, 59].”