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1
Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition ...
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2
Cross-Lingual Text-to-Speech Using Multi-Task Learning and Speaker Classifier Joint Training ...
Yang, J.; He, Lei. - : arXiv, 2022
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3
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian ...
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4
Improving the fusion of acoustic and text representations in RNN-T ...
Zhang, Chao; Li, Bo; Lu, Zhiyun. - : arXiv, 2022
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5
Cross-view Brain Decoding ...
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6
Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model ...
Shen, Ying; Yang, Huiyu; Lin, Lin. - : arXiv, 2022
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7
Learning English with Peppa Pig ...
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8
Separate What You Describe: Language-Queried Audio Source Separation ...
Liu, Xubo; Liu, Haohe; Kong, Qiuqiang. - : arXiv, 2022
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9
Chain-based Discriminative Autoencoders for Speech Recognition ...
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10
Unsupervised word-level prosody tagging for controllable speech synthesis ...
Guo, Yiwei; Du, Chenpeng; Yu, Kai. - : arXiv, 2022
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11
gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach ...
Dornheim, Johannes. - : arXiv, 2022
Abstract: In real-world decision optimization, often multiple competing objectives must be taken into account. Following classical reinforcement learning, these objectives have to be combined into a single reward function. In contrast, multi-objective reinforcement learning (MORL) methods learn from vectors of per-objective rewards instead. In the case of multi-policy MORL, sets of decision policies for various preferences regarding the conflicting objectives are optimized. This is especially important when target preferences are not known during training or when preferences change dynamically during application. While it is, in general, straightforward to extend a single-objective reinforcement learning method for MORL based on linear scalarization, solutions that are reachable by these methods are limited to convex regions of the Pareto front. Non-linear MORL methods like Thresholded Lexicographic Ordering (TLO) are designed to overcome this limitation. Generalized MORL methods utilize function approximation to ...
Keyword: 68T05, 68T42; Artificial Intelligence cs.AI; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; I.2.8; Machine Learning cs.LG; Systems and Control eess.SY
URL: https://dx.doi.org/10.48550/arxiv.2204.04988
https://arxiv.org/abs/2204.04988
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12
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales ...
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13
Improving End-To-End Modeling for Mispronunciation Detection with Effective Augmentation Mechanisms ...
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14
An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation ...
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15
NVC-Net: End-to-End Adversarial Voice Conversion ...
Nguyen, Bac; Cardinaux, Fabien. - : arXiv, 2021
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16
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech ...
Wang, Pengwei; Ye, Xin; Zhou, Xiaohuan. - : arXiv, 2021
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17
NIST SRE CTS Superset: A large-scale dataset for telephony speaker recognition ...
Sadjadi, Seyed Omid. - : arXiv, 2021
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18
Interpreting intermediate convolutional layers of CNNs trained on raw speech ...
Beguš, Gašper; Zhou, Alan. - : arXiv, 2021
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19
A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images ...
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20
A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images ...
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