Mixture autoregressive hidden Markov models for speech signals

By Biing-Hwang Fred Juang, Lawrence R. Rabiner. 1985

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In Natural Language Processing

Citations in the corpus

WaveNet: A Generative Model for Raw Audio (2016, background): “• Gaussian process assumption; The conventional generative models are based on Gaussian process (Itakura & Saito, 1970; Imai & Furuichi, 1988; Poritz, 1982; Juang & Rabiner, 1985; Kameoka et al., 2010; Tokuda & Zen, 2015; 2016).”
Teacher-Student Training For Robust Tacotron-Based TTS (2019, methodology): “A typical neural TTS system suffers from the exposure bias problem [16,17] in the autoregressive model [18] that is used by the decoder module.”
Hidden Markov processes (2002, methodology): “Parametrization of each covariance matrix as a matrix of an autoregressive process of order [149] was studied in [254], [255], [175].”
Spectrally resolved fast transient brain states in electrophysiological data (2016, unknown): “Published by Elsevier Inc a b s t r a c t a r t i c l e i n f o”
Autoregressive Models for Statistical Parametric Speech Synthesis (2013, methodology): “This is distinct from the hidden filter HMM [9], [10] for which the observations are waveform samples.”
MOSAIC Model for Sensorimotor Learning and Control (2001, methodology): “It is related to the methods proposed in the speech processing community (Poritz, 1982; Juang & Rabiner, 1985).”
From HMM's to segment models: a unified view of stochastic modeling for speech recognition (1996, methodology): “Although HMMs will continue to play a role in most recognition systems for a long time to come, many alternative models have been proposed in recent years to address some of the shortcomings of HMMs.”