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1
Emotional Speech Recognition Using Deep Neural Networks
In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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2
Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
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3
Text Data Augmentation for the Korean Language
In: Applied Sciences; Volume 12; Issue 7; Pages: 3425 (2022)
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4
Emotional Speech Recognition Using Deep Neural Networks
In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
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5
A Study of Data Augmentation for ASR Robustness in Low Bit Rate Contact Center Recordings Including Packet Losses
In: Applied Sciences; Volume 12; Issue 3; Pages: 1580 (2022)
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6
Modeling the effect of military oxygen masks on speech characteristics
In: Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03325087 ; Interspeech 2021, Aug 2021, Brno, Czech Republic (2021)
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7
Simulating reading mistakes for child speech Transformer-based phone recognition
In: Annual Conference of the International Speech Communication Association (INTERSPEECH) ; https://hal.archives-ouvertes.fr/hal-03257870 ; Annual Conference of the International Speech Communication Association (INTERSPEECH), Aug 2021, Brno, Czech Republic (2021)
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8
A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild ...
Nunnari, Fabrizio; España-Bonet, Cristina; Avramidis, Eleftherios. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
Abstract: In this paper, we describe the current main approaches to sign language translation which use deep neural networks with videos as input and text as output. We highlight that, under our point of view, their main weakness is the lack of generalization in daily life contexts. Our goal is to build a state-of-the-art system for the automatic interpretation of sign language in unpredictable video framing conditions. Our main contribution is the shift from image features to landmark positions in order to diminish the size of the input data and facilitate the combination of data augmentation techniques for landmarks. We describe the set of hypotheses to build such a system and the list of experiments that will lead us to their verification. ... : OASIcs, Vol. 93, 3rd Conference on Language, Data and Knowledge (LDK 2021), pages 36:1-36:8 ...
Keyword: Computing methodologies → Computer graphics; Computing methodologies → Machine learning; data augmentation; end-to-end translation; Human-centered computing → Accessibility technologies; sing language; video recognition
URL: https://drops.dagstuhl.de/opus/volltexte/2021/14572/
https://dx.doi.org/10.4230/oasics.ldk.2021.36
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9
Effekten av textaugmenteringsstrategier på träffsäkerhet, F1-värde och viktat F1-värde ; The effect of text data augmentation strategies on Accuracy, F1-score, and weighted F1-score
Shmas, George; Svedberg, Jonatan. - : KTH, Hälsoinformatik och logistik, 2021
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10
Using Data Augmentation and Time-Scale Modification to Improve ASR of Children’s Speech in Noisy Environments
In: Applied Sciences ; Volume 11 ; Issue 18 (2021)
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11
Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm
In: Mathematics; Volume 10; Issue 1; Pages: 4 (2021)
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12
Volumetric changes at implant sites: A systematic appraisal of traditional methods and optical scanning- based digital technologies
Tavelli, Lorenzo; Barootchi, Shayan; Majzoub, Jad. - : Wiley Periodicals, Inc., 2021
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13
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
Sánchez-Cartagena, Víctor M.; Sánchez-Martínez, Felipe; Pérez-Ortiz, Juan Antonio. - : Association for Computational Linguistics, 2021
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14
Improving Short Text Classification Through Global Augmentation Methods
In: Lecture Notes in Computer Science ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE) ; https://hal.inria.fr/hal-03414750 ; 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2020, Dublin, Ireland. pp.385-399, ⟨10.1007/978-3-030-57321-8_21⟩ (2020)
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15
Data Augmenting Contrastive Learning of Speech Representations in the Time Domain
In: SLT 2020 - IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070321 ; SLT 2020 - IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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16
Characterization and classification of semantic image-text relations ...
Otto, Christian; Springstein, Matthias; Anand, Avishek. - : London : Springer, 2020
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17
Characterization and classification of semantic image-text relations ...
Otto, C.; Springstein, M.; Anand, A.. - : Berlin : Springer Nature, 2020
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18
Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems
In: Sensors ; Volume 20 ; Issue 9 (2020)
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19
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
In: Fraunhofer IAIS (2020)
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20
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
In: Proceedings of the Society for Computation in Linguistics (2020)
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