Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models

By Kuan-Yu Chen, Che-Ping Tsai, Da-Rong Liu, Hung-yi Lee, Lin-Shan Lee. 2019

19 citations recorded by the source corpus.

Subfields: Generative Models, Speech & Audio

How researchers see this paper

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

In Generative Models

1. Hung-yi Lee
2. Wei-Ning Hsu
3. Abdel-rahman Mohamed
4. Lars Maaløe
5. Christian Igel
6. Jakob Drachmann Havtorn
7. Joakim Edin
8. Lasse Borgholt

In Speech & Audio

1. Wei-Ning Hsu
2. Abdel-rahman Mohamed
3. Alexei Baevski
4. Hung-yi Lee
5. Jinyu Li
6. Michael Auli
7. Alexis Conneau
8. Shang-Wen Li

Citations in the corpus

• Unsupervised Speech Recognition (2021, methodology): “We use adversarial training to train an unsupervised speech recognition model using the representations of the unlabeled speech audio data and the unlabeled phonemized text data (Liu et al., 2018; Chen et al., 2019).”
• Self-Supervised Speech Representation Learning: A Review (2022, methodology): “use a phoneme segmentation module before the generator to segment utterances into phoneme-level segments [283], [290], [293].”