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Found 9 papers, 0 papers with code
Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders
In the context of the SSL model predicting masked frames, the generated global representations are also noted to exhibit task-agnostic traits.
Analyzing the Effect of Data Impurity on the Detection Performances of Mental Disorders
In this study, we investigate this hypothesis in the context of major depressive disorder (MDD) and post-traumatic stress disorder detection (PTSD).
Significance of Data Augmentation for Improving Cleft Lip and Palate Speech Recognition
The automatic recognition of pathological speech, particularly from children with any articulatory impairment, is a challenging task due to various reasons.
Joint Language Identification of Code-Switching Speech using Attention based E2E Network
For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance.
Investigating Target Set Reduction for End-to-End Speech Recognition of Hindi-English Code-Switching Data
End-to-end (E2E) systems are fast replacing the conventional systems in the domain of automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Language Modeling for Code-Switched Data: Challenges and Approaches
Lately, the problem of code-switching has gained a lot of attention and has emerged as an active area of research.
Correlation and Class Based Block Formation for Improved Structured Dictionary Learning
The existing works mostly assume that the block structure is known a priori while learning the dictionary.
An Unsupervised Method for Detection and Validation of The Optic Disc and The Fovea
In this work, we have presented a novel method for detection of retinal image features, the optic disc and the fovea, from colour fundus photographs of dilated eyes for Computer-aided Diagnosis(CAD) system.
A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction
In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction.
