A Semisupervised Approach for Language Identification based on Ladder Networks
In this study we address the problem of training a neuralnetwork for language identification using both labeled and unlabeled speech samples in the form of i-vectors. We propose a neural network architecture that can also handle out-of-set languages. We utilize a modified version of the recently proposed Ladder Network semisupervised training procedure that optimizes the reconstruction costs of a stack of denoising autoencoders. We show that this approach can be successfully applied to the case where the training dataset is composed of both labeled and unlabeled acoustic data. The results show enhanced language identification on the NIST 2015 language identification dataset.
Code (1)
Tasks
DenoisingLanguage IdentificationSimilar Papers 제목 키워드 기반
PSLT: A Light-weight Vision Transformer with Ladder Self-Attention and Progressive Shift
Vision Transformer (ViT) has shown great potential for various visual tasks due to its ability to model long-range dependency. However, ViT requires a large amount of computing resource to compute the global self-attenti…
image-classificationImage ClassificationPerson Re-IdentificationA Survey on Semi-Supervised Learning Techniques
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised lea…
SurveySemisupervised Neural Proto-Language Reconstruction
Existing work implementing comparative reconstruction of ancestral languages (proto-languages) has usually required full supervision. However, historical reconstruction models are only of practical value if they can be t…
Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks
Precise segmentation of bladder walls and tumor regions is an essential step towards non-invasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder c…
PrognosisSegmentationGallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN
Medical image analysis is a significant application of artificial intelligence for disease diagnosis. A crucial step in this process is the identification of regions of interest within the images. This task can be automa…
Cancer ClassificationGallbladder Cancer DetectionMedical Image Analysisobject-detection+1