An unsupervised deep domain adaptation approach for robust speech recognition

By Sining Sun, Binbin Zhang, Lei Xie, Yanning Zhang. 2017

164 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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. Jinyu Li
2. James R. Glass
3. Wei-Ning Hsu
4. Dong Yu
5. Yu Zhang
6. David F. Harwath
7. Hung-yi Lee
8. Yu-An Chung

In Signal Processing

1. Jinyu Li
2. James R. Glass
3. Dong Yu
4. Wei-Ning Hsu
5. Yifan Gong
6. Zhuo Chen
7. David F. Harwath
8. Yu Zhang

Citations in the corpus

• Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (2021, neutral): “This includes data augmentation [13, 14], self-training on target domain [15], domain adversarial training [16], and joint training [8].”
• Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition (2018, background): “Alternatively, robustness can also be achieved by training using features that are domain invariant [10, 11, 12, 13, 14].”
• Data Efficient Voice Cloning from Noisy Samples with Domain Adversarial Training (2020, background): “Note that DAT has been previously studied in speech recognition [17, 18, 19], speaker verification [20, 21] as well as speech synthesis [15, 10] tasks with superior performance in learning noise-invariant features and attribute disentanglement.”
• Unsupervised adaptation with domain separation networks for robust speech recognition (2017, methodology): “This GRL approach has been applied to acoustic models for unsupervised adaptation in [25] and for increasing noise robustness in [26, 27].”
• Disentangling Correlated Speaker and Noise for Speech Synthesis via Data Augmentation and Adversarial Factorization (2019, methodology): “These two 109 methods have been mainly applied for training robust discriminative models [9, 24, 26, 21], and 110 are less studied in the context of building generative models.”
• Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling (2019, unknown): “Motivated by successful applications of adversarial training [20] in a wide range of domain invariant learning tasks [21–24], this work proposes to add an auxiliary adversarial speaker classification task to explicitly target speaker-invariant feature learning.”
• Conditional Teacher-student Learning (2019, background): “T/S learning [3, 8, 9] and adversarial learning [20, 21, 22, 23, 24] are two effective approaches that can suppress this domain mismatch by adapting a source-domain acoustic model to target-domain speech.”
• VOICe: A Sound Event Detection Dataset For Generalizable Domain Adaptation (2019, background): “chine vision [13, 14], speech recognition [15, 16], and audio classification [17].”
• Recent progresses in deep learning based acoustic models (2017, background): “Recently, the concept of adversarial training [125] was explored for noise-robust ASR [126]–[128].”
• A Review of Domain Adaptation without Target Labels (2019, background): “They have also been applied to a variety of problem settings, such as speech recognition [163], medical image segmentation [41] and cross-language knowledge transfer [164].”
• Speaker-Invariant Training Via Adversarial Learning (2018, methodology): “In speech, it has been applied to noise-robust [15, 16, 17, 18, 19] and conversational ASR [20] using gradient reversal layer [21] or domain separation network [22].”
• Transfer Learning from Audio-Visual Grounding to Speech Recognition (2019, background): “Furthermore, this learning can take place jointly with ASR [15], or separately with some tasks that have aligned objectives [16, 17, 18].”