Unsupervised training of an HMM-based speech recognizer for topic classification

By Herbert Gish, Man-Hung Siu, Arthur Chan, William Belfield. 2009

34 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Natural Language Processing, Speech & Audio

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Citations in the corpus

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).”
A segmental framework for fully-unsupervised large-vocabulary speech recognition (2016, background): “spoken documents [2, 3, 24], speech-to-speech translation of low-resource languages [25, 26], language recognition [27], and in improving purely supervised keyword search systems [7].”
An embedded segmental K-means model for unsupervised segmentation and clustering of speech (2017, background): “com/kamperh/recipe_zs2017_track2 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].”
Towards Visually Grounded Sub-word Speech Unit Discovery (2019, background): “Other works have jointly learned subword as well as word-level units in a unified framework [8, 9].”
Fully unsupervised small-vocabulary speech recognition using a segmental Bayesian model (2015, background): “Recent studies in the speech processing community have applied unsupervised techniques to tasks such as phonetic discovery [1, 2, 3, 4], lexical discovery [5, 6, 7], spoken document retrieval [8] and query-by-example search [9, 10].”
Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery (2014, methodology): “The n-gram statistics can either be extracted from recognition 1-best hypotheses as in Gish et al. (2009), or more generally, from the quinphone re-scored lattices (Siu et al., 2010), which are soft counts based on the lattice posterior probabilities.”
Unsupervised neural network based feature extraction using weak top-down constraints (2015, background): “Zero-resource technology aims to solve tasks such as phonetic and lexical discovery [4, 5], spoken document retrieval [6], and query-by-example search [7, 8] by using only raw speech data.”