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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 ...
Abstract: Attempts to computationally simulate the acquisition of spoken language via grounding in perception have a long tradition but have gained momentum in the past few years. Current neural approaches exploit associations between the spoken and visual modality and learn to represent speech and visual data in a joint vector space. A major unresolved issue from the point of ecological validity is the training data, typically consisting of images or videos paired with spoken descriptions of what is depicted. Such a setup guarantees an unrealistically strong correlation between speech and the visual world. In the real world the coupling between the linguistic and the visual is loose, and often contains confounds in the form of correlations with non-semantic aspects of the speech signal. The current study is a first step towards simulating a naturalistic grounding scenario by using a dataset based on the children's cartoon Peppa Pig. We train a simple bi-modal architecture on the portion of the data consisting of ...
Keyword: Artificial Intelligence cs.AI; Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Image and Video Processing eess.IV
URL: https://dx.doi.org/10.48550/arxiv.2202.12917
https://arxiv.org/abs/2202.12917
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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
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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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