A Tutorial on Hidden Markov Models and Selected Applications

By Lawrence R. Rabiner. 1989

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Citations in the corpus

Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech (2021, methodology): “Glow-TTS model made use of Monotonic Alignment Search algorithm (an adoption of Viterbi training (Rabiner, 1989) finding the most likely hidden alignment between two sequences) proposed to map the input text to melspectrograms efficiently.”
Hidden Markov models in computational biology. Applications to protein modeling. (1993, methodology): “In addition, with a simple extension to the algorithm, the most probable path itself can be found using the usual backtracking technique (Rabiner, 1989).”
Sequential Monte Carlo methods for dynamic systems (1997, methodology): “The main diiculty is that analytical formulas for this recursive updating only exist for certain exponential family models (West and Harrison 1989) or nite discrete-state space model (Rabiner 1989).”
Selective review of offline change point detection methods (2020, methodology): “The ost well-known Bayesian algorithm is the Hidden Markov Model HMM) [38] .”
Action Recognition from Arbitrary Views using 3D Exemplars (2007, unknown): “Using the classical HMM algorithms [15], such a probability can be efﬁciently computed under the following conditions: First, we use a small set of exemplars that is shared by all models.”
Constructing free-energy approximations and generalized belief propagation algorithms (2005, methodology): “They are well known by names like the forward–backward algorithm for hidden Markov models [3], the Viterbi algorithm [4], [5], Gallager’s sum–product algorithm for decoding lowdensity parity check codes [6], the “turbo-decoding” algorithm [7], [8], Pearl’s “belief propagation” algorithm for inferenc”
RAD-TTS: Parallel Flow-Based TTS with Robust Alignment Learning and Diverse Synthesis (2021, methodology): “In order to learn to align X with Φ , we combine the Viterbi and forward-backward algorithms used in Hidden Markov Models (HMMs) (Rabiner, 1990) in order to learn hard and soft alignments, respectively.”
Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making (2001, methodology): “Chrisman's work is based on the Baum-Welch algorithm, which was originally proposed for learning hidden Markov models (HMMs) (Rabiner 1989).”