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Simple Search
Hits 1 – 11 of 11
1
Simple and Effective Unsupervised Speech Synthesis ...
Liu, Alexander H.
;
Lai, Cheng-I Jeff
;
Hsu, Wei-Ning
. - : arXiv, 2022
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2
Text-Free Image-to-Speech Synthesis Using Learned Segmental Units ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Glass, James
;
Harwath, David
. - : Underline Science Inc., 2021
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3
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning
Khurana, Sameer
;
Laurent, Antoine
;
Hsu, Wei-Ning
...
In: Interspeech 2020 ; https://hal.archives-ouvertes.fr/hal-02912029 ; Interspeech 2020, Oct 2020, Shanghai, China (2020)
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4
A Convolutional Deep Markov Model for Unsupervised Speech Representation Learning ...
Khurana, Sameer
;
Laurent, Antoine
;
Hsu, Wei-Ning
;
Chorowski, Jan
;
Lancucki, Adrian
;
Marxer, Ricard
;
Glass, James
. - : arXiv, 2020
Abstract:
Probabilistic Latent Variable Models (LVMs) provide an alternative to self-supervised learning approaches for linguistic representation learning from speech. LVMs admit an intuitive probabilistic interpretation where the latent structure shapes the information extracted from the signal. Even though LVMs have recently seen a renewed interest due to the introduction of Variational Autoencoders (VAEs), their use for speech representation learning remains largely unexplored. In this work, we propose Convolutional Deep Markov Model (ConvDMM), a Gaussian state-space model with non-linear emission and transition functions modelled by deep neural networks. This unsupervised model is trained using black box variational inference. A deep convolutional neural network is used as an inference network for structured variational approximation. When trained on a large scale speech dataset (LibriSpeech), ConvDMM produces features that significantly outperform multiple self-supervised feature extracting methods on linear ... : Proceedings of Interspeech, 2020 ...
Keyword:
Audio and Speech Processing eess.AS
;
Computation and Language cs.CL
;
FOS Computer and information sciences
;
FOS Electrical engineering, electronic engineering, information engineering
;
Machine Learning cs.LG
;
Sound cs.SD
URL:
https://arxiv.org/abs/2006.02547
https://dx.doi.org/10.48550/arxiv.2006.02547
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5
Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech ...
Harwath, David
;
Hsu, Wei-Ning
;
Glass, James
. - : arXiv, 2019
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6
Transfer Learning from Audio-Visual Grounding to Speech Recognition ...
Hsu, Wei-Ning
;
Harwath, David
;
Glass, James
. - : arXiv, 2019
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7
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition ...
Hsu, Wei-Ning
;
Tang, Hao
;
Glass, James
. - : arXiv, 2018
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8
Unsupervised Representation Learning of Speech for Dialect Identification ...
Shon, Suwon
;
Hsu, Wei-Ning
;
Glass, James
. - : arXiv, 2018
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9
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data ...
Hsu, Wei-Ning
;
Zhang, Yu
;
Glass, James
. - : arXiv, 2017
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10
Learning Latent Representations for Speech Generation and Transformation ...
Hsu, Wei-Ning
;
Zhang, Yu
;
Glass, James
. - : arXiv, 2017
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11
Recurrent Neural Network Encoder with Attention for Community Question Answering ...
Hsu, Wei-Ning
;
Zhang, Yu
;
Glass, James
. - : arXiv, 2016
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