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)
|
|
BASE
|
|
Show 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)
|
|
Abstract:
Applications for facial recognition have eased the process of personal identification. However, there are increasing concerns about the performance of these systems against the challenges of presentation attacks, spoofing, and disguises. One of the reasons for the lack of a robustness of facial recognition algorithms in these challenges is the limited amount of suitable training data. This lack of training data can be addressed by creating a database with the subjects having several disguises, but this is an expensive process. Another approach is to use generative adversarial networks to synthesize facial images with the required disguise add-ons. In this paper, we present a synthetic disguised face database for the training and evaluation of robust facial recognition algorithms. Furthermore, we present a methodology for generating synthetic facial images for the desired disguise add-ons. Cycle-consistency loss is used to generate facial images with disguises, e.g., fake beards, makeup, and glasses, from normal face images. Additionally, an automated filtering scheme is presented for automated data filtering from the synthesized faces. Finally, facial recognition experiments are performed on the proposed synthetic data to show the efficacy of the proposed methodology and the presented database. Training on the proposed database achieves an improvement in the rank-1 recognition rate (68.3%), over a model trained on the original nondisguised face images.
|
|
Keyword:
CycleGAN; data augmentation; disguised face; generative adversarial networks; Sejong Face Database; style transfer; synthetic database; Synthetic Disguised Face Database; synthetic faces
|
|
URL: https://doi.org/10.3390/math10010004
|
|
BASE
|
|
Hide 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
|
|
|
|