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)
|
|
BASE
|
|
Show details
|
|
2 |
Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System
|
|
|
|
In: Sustainability; Volume 14; Issue 2; Pages: 614 (2022)
|
|
BASE
|
|
Show details
|
|
3 |
Text Data Augmentation for the Korean Language
|
|
|
|
In: Applied Sciences; Volume 12; Issue 7; Pages: 3425 (2022)
|
|
Abstract:
Data augmentation (DA) is a universal technique to reduce overfitting and improve the robustness of machine learning models by increasing the quantity and variety of the training dataset. Although data augmentation is essential in vision tasks, it is rarely applied to text datasets since it is less straightforward. Some studies have concerned text data augmentation, but most of them are for the majority languages, such as English or French. There have been only a few studies on data augmentation for minority languages, e.g., Korean. This study fills the gap by demonstrating several common data augmentation methods and Korean corpora with pre-trained language models. In short, we evaluate the performance of two text data augmentation approaches, known as text transformation and back translation. We compare these augmentations among Korean corpora on four downstream tasks: semantic textual similarity (STS), natural language inference (NLI), question duplication verification (QDV), and sentiment classification (STC). Compared to cases without augmentation, the performance gains when applying text data augmentation are 2.24%, 2.19%, 0.66%, and 0.08% on the STS, NLI, QDV, and STC tasks, respectively.
|
|
Keyword:
data augmentation; Korean language processing; language modeling
|
|
URL: https://doi.org/10.3390/app12073425
|
|
BASE
|
|
Hide details
|
|
4 |
Emotional Speech Recognition Using Deep Neural Networks
|
|
|
|
In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
8 |
A Data Augmentation Approach for Sign-Language-To-Text Translation In-The-Wild ...
|
|
|
|
BASE
|
|
Show details
|
|
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
|
|
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
11 |
Generating Synthetic Disguised Faces with Cycle-Consistency Loss and an Automated Filtering Algorithm
|
|
|
|
In: Mathematics; Volume 10; Issue 1; Pages: 4 (2021)
|
|
BASE
|
|
Show details
|
|
12 |
Volumetric changes at implant sites: A systematic appraisal of traditional methods and optical scanning- based digital technologies
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach
|
|
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
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)
|
|
BASE
|
|
Show details
|
|
16 |
Characterization and classification of semantic image-text relations ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Characterization and classification of semantic image-text relations ...
|
|
|
|
BASE
|
|
Show details
|
|
18 |
Using Complexity-Identical Human- and Machine-Directed Utterances to Investigate Addressee Detection for Spoken Dialogue Systems
|
|
|
|
In: Sensors ; Volume 20 ; Issue 9 (2020)
|
|
BASE
|
|
Show details
|
|
19 |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
|
|
|
|
In: Fraunhofer IAIS (2020)
|
|
BASE
|
|
Show details
|
|
20 |
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity
|
|
|
|
In: Proceedings of the Society for Computation in Linguistics (2020)
|
|
BASE
|
|
Show details
|
|
|
|