Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery

By Janek Ebbers, Jahn Heymann, Lukas Drude, Thomas Glarner, Reinhold Häb-Umbach, Bhiksha Raj. 2017

47 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Generative Models, Speech & Audio, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wei-Ning Hsu
2. Yossi Adi
3. Gabriel Synnaeve
4. James R. Glass
5. Abdel-rahman Mohamed
6. Emmanuel Dupoux
7. Jade Copet
8. Yoshua Bengio

In Generative Models

1. Wei-Ning Hsu
2. Yossi Adi
3. Jade Copet
4. James R. Glass
5. Abdel-rahman Mohamed
6. Gabriel Synnaeve
7. Emmanuel Dupoux
8. Sameer Khurana

In Speech & Audio

1. Wei-Ning Hsu
2. Yossi Adi
3. Gabriel Synnaeve
4. Jade Copet
5. Abdel-rahman Mohamed
6. Emmanuel Dupoux
7. Evgeny Kharitonov
8. Tu Nguyen

In Signal Processing

1. Wei-Ning Hsu
2. Yossi Adi
3. Emmanuel Dupoux
4. Abdel-rahman Mohamed
5. Jade Copet
6. James R. Glass
7. Gabriel Synnaeve
8. Evgeny Kharitonov

Citations in the corpus

• HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units (2021, background): “More advanced systems can achieve better acoustic unit discovery performance using better graphical models [27], [28] or parameterizes the”
• Speech Resynthesis from Discrete Disentangled Self-Supervised Representations (2021, background): “Various constraints can be imposed on the encoded space, such as temporal smoothness [10], discreteness [11], and hierarchy [12].”
• On Generative Spoken Language Modeling from Raw Audio (2021, neutral): “Various constraints can be imposed to the encoded space, such as temporal smoothness (Ebbers et al., 2017; Glarner et al., 2018; Khurana et al., 2019, 2020), discreteness (Ondel et al.”
• AudioGen: Textually Guided Audio Generation (2022, background): “Various constraints can be imposed on the encoded space, such as temporal smoothness (Ebbers et al., 2017), discreteness (van den Oord et al., 2017b), and hierarchy (Hsu et al., 2017).”
• Hubert: How Much Can a Bad Teacher Benefit ASR Pre-Training? (2021, background): “The earliest line of work relies on generative modeling with latent variables [24, 25, 8, 26, 27, 28].”
• A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning (2020, methodology): “Structured VAE has also been used for acoustic unit discovery [27], which is not the focus of this work.”
• Textless Speech Emotion Conversion using Decomposed and Discrete Representations (2021, background): “Various constraints can be imposed on the encoded space, such as temporal smoothness (Ebbers et al., 2017; Glarner et al., 2018; Khurana et al., 2020), discreteness (Ondel et al.”
• A Brief Overview of Unsupervised Neural Speech Representation Learning (2022, unknown): “Some LVMs do not use autoregressive observation models (Ebbers et al. 2017; Glarner et al. 2018; Hsu, Zhang, and Glass 2017b,a; Khurana et al. 2019, 2020).”