Feature optimized DPGMM clustering for unsupervised subword modeling: A contribution to zerospeech 2017

By Michael Heck, Sakriani Watiasri Sakti, Satoshi Nakamura. 2017

54 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Speech & Audio, Signal Processing

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Citations in the corpus

Unsupervised Pretraining Transfers Well Across Languages (2020, neutral): “[26] 8.”
Libri-Light: A Benchmark for ASR with Limited or No Supervision (2019, neutral): “CPC constructs embeddings with good ABX scores compared to an MFCC baseline, and are in the same range as the SOTA in the Zero Resource Speech Challenge 2017 for English (6.2% within and 8.7% across [24]).”
VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019 (2019, methodology): “The top performances in discovering speech representation in ZeroSpeech 2015 and 2017 are dominated by a Bayesian non-parametric approach with unsupervised cluster speech features using a Dirichlet process Gaussian mixture model (DPGMM) [4, 5].”
The Zero Resource Speech Challenge 2019: TTS without T (2019, methodology): “On the acoustic unit discovery side, several methods have been used (binarized autoencoders [7], binarized siamese networks [8]; a variety of speaker normalization techniques have been used to improve the categories [9], among other techniques).”
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain (2020, neutral): “Yet, CPC has still not reached state-of-the-art performance in the pure zero resource setting and is beaten by comparatively simpler systems like DPGMM [12, 13] which cannot be fine tuned for other downstream tasks.”
Towards Unsupervised Learning of Speech Features in the Wild (2021, methodology): “While such techniques have been applied to unsupervised representation learning [23, 24, 25] to our knowledge, they have not been tested with predictive learning.”
Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders (2019, background): “…et al., 2015; Zeghidour et al., 2016; Heck et al., 2016; Srivastava and Shrivastava, 2016; Kamper et al., 2017b; Chen et al., 2017; Yuan et al., 2017; Heck et al., 2017; Shibata et al., 2017; Ansari et al., 2017a,b) perform unsupervised ABX discrimination and/or spoken term discovery on the basis…”
Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling (2019, background): “[6] estimated fMLLR features in an unsupervised manner.”
Cyclic Spectral Modeling for Unsupervised Unit Discovery into Voice Conversion with Excitation and Waveform Modeling (2020, background): “A lot of work have been proposed for the unsupervised discovery of subword units [3, 6, 8, 11, 12, 13].”
Self-Supervised Speech Representation Learning: A Review (2022, background): “Following the success of representation learning, baseline and challenge techniques have shifted from purely generative models [327], [328], deep autoencoders [83], [329], and incorporation of neural-network-”
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).”