A Bidirectional Target Filtering Model of Speech Coarticulation: two-stage Implementation for Phonetic Recognition

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Deep Learning: Methods and Applications (2014, methodology): “Further, the DNN results were found to be slightly superior to the then-best-performing single system based on the generative hidden trajectory model (HTM) in the literature [105, 108] evaluated on the same, commonly used TIMIT task by many speech researchers [107, 108, 274, 313].”
A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion (2013, background): “An example of such speech variability is formant undershooting (or overshooting) caused by casual (or forced) speaking styles that have been mathematically represented by hidden dynamic or trajectory models of speech [11][12][13] [14].”
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].”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, background): “…trajectory models [38], hidden dynamic models [39]–[45], buried Markov models [46], structured speech model [47], and hidden trajectory model [48] depending on different " prior knowledge " applied to the temporal structure of speech and on various simplifying assumptions to facilitate the…”