A Nonparametric Bayesian Approach to Acoustic Model Discovery

By Chia-ying Lee, James R. Glass. 2012

231 citations recorded by the source corpus.

Subfields: Probabilistic & Bayesian Methods, Speech & Audio

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Citations in the corpus

HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units (2021, background): “Nevertheless, simple discrete latent variable models such as k-means and Gaussian mixture models (GMMs) infer hidden units that exhibit non-trivial correlation with the underlying acoustic units [26] (see also Table V).”
Unsupervised Speech Recognition (2021, methodology): “Speech data has been segmented and clustered in a large body of prior work (Varadarajan et al., 2008; Zhang and Glass, 2009; Gish et al., 2009; Lee and Glass, 2012; Lee et al., 2015; Ondel et al., 2016), including Bayesian methods (Kamper et al.”
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech (2019, background): “A different line of work employs probabilistic graphical models to jointly segment and cluster the speech signal (Varadarajan et al., 2008; Zhang & Glass, 2009; Gish et al., 2009; Lee & Glass, 2012; Siu et al., 2014; Lee et al., 2015; Ondel et al., 2016; Kamper et al., 2016; 2017a).”
Unsupervised Speech Representation Learning Using WaveNet Autoencoders (2019, methodology): “Extending it with an HMM temporal structure for sub-phonetic units leads to the DP-HMM and the HDP-HMM [69]–[71].”
On Generative Spoken Language Modeling from Raw Audio (2021, neutral): “Various constraints can be imposed on the encoded space, such as temporal smoothness (Ebbers et al., 2017; Glarner et al., 2018; Khurana et al., 2019, 2020), discrete-ness (Ondel et al., 2016; van den Oord et al., 2017), and presence of hierarchy (Lee and Glass, 2012; Hsu et al., 2017b).”
A segmental framework for fully-unsupervised large-vocabulary speech recognition (2016, methodology): “…(GMMs) to produce frame-level posteriorgrams [9,10], us-ing unsupervised hidden Markov models (HMMs) to obtain discrete categorical output in terms of discovered subword units [2,11,12], and using unsupervised neural networks (NNs) to obtain frame-level continuous vector representations [13–15].”
Jointly Discovering Visual Objects and Spoken Words from Raw Sensory Input (2018, background): “Popular approaches are based on dynamic time warping (Jansen et al. 2010; Jansen and Van Durme 2011; Park and Glass 2008), or Bayesian generative models of the speech signal (Kamper et al. 2016; Lee and Glass 2012; Ondel et al. 2016).”
An embedded segmental K-means model for unsupervised segmentation and clustering of speech (2017, background): “Several zero-resource tasks have been studied, including acoustic unit discovery [4–6], unsupervised representation learning [7–9], query-by-example search [10, 11] and topic modelling [12, 13].”
Learning Word-Like Units from Joint Audio-Visual Analysis (2017, methodology): “Alternative approaches based on Bayesian nonparametric modeling (Lee & Glass, 2012; Ondel et al., 2016) employed a generative model to cluster acoustic segments into phoneme-like categories, and related works aimed to segment and cluster either reference or learned phoneme-like tokens into…”
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning (2020, background): “One of the long-standing goals of speech and cognitive scientists is to develop a computational model of language acquisition [1, 2, 3, 4].”
Discovering discrete subword units with binarized autoencoders and hidden-Markov-model encoders (2015, methodology): “In [7] the three sub-tasks are carried out at once us-ing a Dirichlet Process mixture model.”
A Brief Overview of Unsupervised Neural Speech Representation Learning (2022, background): “Non-neural LVMs for speech representation learning have also been explored (Lee and Glass 2012; Ondel, Burget, and Cernocký 2016; Heck, Sakti, and Nakamura 2017; Jansen, Thomas, and Hermansky 2013).”
An auto-encoder based approach to unsupervised learning of subword units (2014, methodology): “Different approaches to subword unit generation based on Hidden Markov Models and GMMs have been proposed for various tasks ranging from keyword spotting to large vocabulary speech recognition [6, 7, 8, 9, 10, 11].”
Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation (2019, background): “1 Probabilistic Bayesian models are developed to construct a model which learns segmentation and representation (or clustering) jointly [21], [25], [64].”
Towards Visually Grounded Sub-word Speech Unit Discovery (2019, background): “Note that [4] reflects scores on the training set, not the testing set.”