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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
. - : arXiv, 2020
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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
Abstract:
We apply a general recurrent neural network (RNN) encoder framework to community question answering (cQA) tasks. Our approach does not rely on any linguistic processing, and can be applied to different languages or domains. Further improvements are observed when we extend the RNN encoders with a neural attention mechanism that encourages reasoning over entire sequences. To deal with practical issues such as data sparsity and imbalanced labels, we apply various techniques such as transfer learning and multitask learning. Our experiments on the SemEval-2016 cQA task show 10% improvement on a MAP score compared to an information retrieval-based approach, and achieve comparable performance to a strong handcrafted feature-based method. ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
;
Machine Learning cs.LG
;
Neural and Evolutionary Computing cs.NE
URL:
https://dx.doi.org/10.48550/arxiv.1603.07044
https://arxiv.org/abs/1603.07044
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