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Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition
[article]
2018
arXiv
pre-print
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In this paper, we investigate the use of adversarial learning for unsupervised adaptation to unseen recording conditions, more specifically, single microphone far-field speech. ...
We adapt neural networks based acoustic models trained with close-talk clean speech to the new recording conditions using untranscribed adaptation data. ...
Crosslingual Adaptation
Conclusions The present study was designed to gain a better understanding of ability of unsupervised domain adaptation by adversarial Learning to improve robustness of ASR. ...
arXiv:1807.11284v1
fatcat:zurxttzyejf67gdrlnzh7wu4wy
Domain Adversarial Training for Accented Speech Recognition
[article]
2018
arXiv
pre-print
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Furthermore, we find that DAT is superior to multi-task learning for accented speech recognition. ...
In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. ...
Among these, domain adaptation is also of great interest for robust speech recognition, especially for DL-based methods. ...
arXiv:1806.02786v1
fatcat:cmdie6p2jngblhyejfolefl2qy
Unsupervised adaptation with domain separation networks for robust speech recognition
2017
2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
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training method, for automatic speech recognition on CHiME-3 dataset. ...
Unsupervised domain adaptation of speech signal aims at adapting a well-trained source-domain acoustic model to the unlabeled data from target domain. ...
In this work, we propose to apply DSN for unsupervised domain adaptation on a DNN-hidden Markov model (HMM) acoustic model, aiming to increase the noise robustness in speech recognition. ...
doi:10.1109/asru.2017.8268938
dblp:conf/asru/MengCMLG17
fatcat:i2h33gjzuzfynfyhrf5yv2vkse
Unsupervised Adversarial Domain Adaptation for Cross-Lingual Speech Emotion Recognition
[article]
2020
arXiv
pre-print
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Cross-lingual speech emotion recognition (SER) is a crucial task for many real-world applications. ...
Therefore, in this paper, we propose a Generative Adversarial Network (GAN)-based model for multilingual SER. ...
CONCLUSIONS In this paper, we proposed an unsupervised adversarial domain adaption approach for developing deep learning models for cross-lingual speech emotion recognition tasks. ...
arXiv:1907.06083v4
fatcat:pbxexa5ydzdqlj63spueap5cga
Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation
2018
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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In this work, we advance T/S learning by proposing adversarial T/S learning to explicitly achieve condition-robust unsupervised domain adaptation. ...
The teacher-student (T/S) learning has been shown effective in unsupervised domain adaptation [1]. ...
To benefit from both methods, in this work, we advance T/S learning with adversarial T/S training for condition-robust unsupervised domain adaptation, where a student acoustic model and a domain classifier ...
doi:10.1109/icassp.2018.8461682
dblp:conf/icassp/MengLGJ18
fatcat:qj2osqass5anti5s5q7h54irti
Adversarial Multi-Task Learning of Deep Neural Networks for Robust Speech Recognition
2016
Interspeech 2016
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A method of learning deep neural networks (DNNs) for noise robust speech recognition is proposed. ...
In this paper, we propose adversarial multi-task learning of DNNs for explicitly enhancing the invariance of representations. ...
[15, 16] for unsupervised domain adaptation, but its application to supervised learning tasks has never been examined before. ...
doi:10.21437/interspeech.2016-879
dblp:conf/interspeech/Shinohara16a
fatcat:p7hrt75grjcx7cwvaaagywhqom
Unsupervised Adaptation with Adversarial Dropout Regularization for Robust Speech Recognition
2019
Interspeech 2019
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Recent adversarial methods proposed for unsupervised domain adaptation of acoustic models try to fool a specific domain discriminator and learn both senone-discriminative and domaininvariant hidden feature ...
Thus, ambiguous target domain features can be generated near the decision boundaries, decreasing speech recognition performance. ...
Acknowledgements This research work is supported by the National Natural Science Foundation of China (No.61571363). ...
doi:10.21437/interspeech.2019-2544
dblp:conf/interspeech/GuoS019
fatcat:twkl6br2cbgt7dkhq6vgsl6rua
Senone-aware Adversarial Multi-task Training for Unsupervised Child to Adult Speech Adaptation
[article]
2021
arXiv
pre-print
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In this work, we propose a feature adaptation approach by exploiting adversarial multi-task training to minimize acoustic mismatch at the senone (tied triphone states) level between adult and child speech ...
Acoustic modeling for child speech is challenging due to the high acoustic variability caused by physiological differences in the vocal tract. ...
UNSUPERVISED FEATURE ADAPTATION VIA ADVERSARIAL MULTI-TASK LEARNING
Feature adaptation with binary domain adversaries The idea of domain adversarial learning is minimizing the domain discriminator prediction ...
arXiv:2102.11488v1
fatcat:b4qbujxjv5gorgjoqn4cfzphma
A Multi-Discriminator CycleGAN for Unsupervised Non-Parallel Speech Domain Adaptation
2018
Interspeech 2018
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Domain adaptation plays an important role for speech recognition models, in particular, for domains that have low resources. ...
We propose a novel generative model based on cyclicconsistent generative adversarial network (CycleGAN) for unsupervised non-parallel speech domain adaptation. ...
Therefore, an unsupervised domain adaptation is desirable for building a robust ASR system. ...
doi:10.21437/interspeech.2018-1535
dblp:conf/interspeech/Hosseini-AslZXS18
fatcat:d76yrtaunjcthldvvb4mt5ddfm
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
[article]
2021
arXiv
pre-print
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distinct research areas including Automatic Speech Recognition (ASR), Speaker Recognition (SR), and Speaker Emotion Recognition (SER). ...
Recent reviews in speech have been conducted for ASR, SR, and SER, however, none of these has focused on the representation learning from speech -- a gap that our survey aims to bridge. ...
Also,
some studies [182], [183] explored unsupervised representation
learning-based domain adaptation for distant conversational
speech recognition. ...
arXiv:2001.00378v2
fatcat:ysvljxylwnajrbowd3kfc7l6ve
Adversarial Learning of Raw Speech Features for Domain Invariant Speech Recognition
[article]
2018
arXiv
pre-print
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Promising empirical results indicate the strength of adversarial training for unsupervised domain adaptation in ASR, thereby emphasizing the ability of DANNs to learn domain invariant features from raw ...
This paper explores the application of adversarial training to learn features from raw speech that are invariant to acoustic variability. ...
Sun et.al. [16] arXiv:1805.08615v1 [eess.AS] 21 May 2018 used adversarial training for unsupervised domain adaptation for robust speech recognition using filter bank features and used the WSJ and Librispeech ...
arXiv:1805.08615v1
fatcat:ehbdqulelfdgbk4yqtlfitvbau
Cross-lingual Text-independent Speaker Verification Using Unsupervised Adversarial Discriminative Domain Adaptation
2019
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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2019 pre-print arXiv:1908.01447v1 fatcat:ttycx7ucejehnpquakg3bxzkly
In this study, we introduce an unsupervised Adversarial Discriminative Domain Adaptation (ADDA) method to effectively learn an asymmetric mapping that adapts the target domain encoder to the source domain ...
Further data analysis of ADDA adapted speaker embedding shows that the learned speaker embeddings can perform well on speaker classification for the target domain data, and are less dependent with respect ...
SRE corpus is part of the Mixer 6 project, which was designed to support the development of robust speaker recognition technology by providing carefully collected speech across numerous microphones. ...
doi:10.1109/icassp.2019.8682259
dblp:conf/icassp/XiaHH19
fatcat:ulwrq5klbbad7dfbzdtg3b6sga
Unsupervised Feature Adaptation Using Adversarial Multi-Task Training for Automatic Evaluation of Children's Speech
2020
Interspeech 2020
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In this work, we tackle such challenges by proposing an unsupervised feature adaptation approach based on adversarial multi-task training in a neural framework. ...
Experimental results demonstrate that our proposed approach consistently outperforms established baselines trained on adult speech across a variety of tasks ranging from speech recognition to pronunciation ...
classification [16] , robust speech recognition [17] , speaker adaptation [18] , and spoken keyword spotting [19] . ...
doi:10.21437/interspeech.2020-1657
dblp:conf/interspeech/DuanC20
fatcat:fmyri7jmwbazfpsztsc3awihd4
Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition
[article]
2019
arXiv
pre-print
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We improved word accuracies by 6.55~15.70\% for the CHiME4 challenge corpus by applying a noisy-to-clean environment adaptation for robust ASR. ...
In this paper, we propose a domain adaptation method based on generative adversarial nets (GANs) with disentangled representation learning to achieve robustness in ASR systems. ...
) [11] was applied to learn a mapping for speech-tospeech adaptation from a speaker to a target speaker for gendermismatched recognition [12] . ...
arXiv:1904.06086v1
fatcat:qnak75aupfeqrd6q47n2ymmno4
Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features
[article]
2021
arXiv
pre-print
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As a result, domain adaptation is important in speech recognition where we train the model for a particular source domain and test it on a different target domain. ...
In this paper, we propose a technique to perform unsupervised gender-based domain adaptation in speech recognition using phonetic features. ...
Approaches to explore the gender based domain adaptation for speech recognition include cycle-GAN [7] , multi-discriminator cycle-GAN [8] and augmented cycle adversarial learning [9] . ...
arXiv:2108.02850v1
fatcat:mp4prfyp4repvbjtaf2qwy7qmu
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