An Overview of Techniques for Biomarker Discovery in Voice Signal
- URL: http://arxiv.org/abs/2110.04678v1
- Date: Sun, 10 Oct 2021 01:39:28 GMT
- Title: An Overview of Techniques for Biomarker Discovery in Voice Signal
- Authors: Rita Singh, Ankit Shah, Hira Dhamyal
- Abstract summary: It presents three categories of techniques that can potentially uncover such elusive biomarkers.
These approaches include proxy techniques, model-based analytical techniques and data-driven AI techniques.
- Score: 13.779629490728759
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper reflects on the effect of several categories of medical conditions
on human voice, focusing on those that may be hypothesized to have effects on
voice, but for which the changes themselves may be subtle enough to have eluded
observation in standard analytical examinations of the voice signal. It
presents three categories of techniques that can potentially uncover such
elusive biomarkers and allow them to be measured and used for predictive and
diagnostic purposes. These approaches include proxy techniques, model-based
analytical techniques and data-driven AI techniques.
Related papers
- Survey on biomarkers in human vocalizations [4.697541589139523]
Survey paper proposes a general taxonomy of the technologies and a broad overview of current progress and challenges.
Vocal biomarkers are often secondary measures that are approximating a signal of another sensor or identifying an underlying mental, cognitive, or physiological state.
arXiv Detail & Related papers (2024-07-07T08:09:28Z) - A Novel Labeled Human Voice Signal Dataset for Misbehavior Detection [0.7223352886780369]
This research highlights the significance of voice tone and delivery in automated machine-learning systems for voice analysis and recognition.
It contributes to the broader field of voice signal analysis by elucidating the impact of human behaviour on the perception and categorization of voice signals.
arXiv Detail & Related papers (2024-06-28T18:55:07Z) - Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives [2.608119698700597]
This review aims to comprehensively cover advancements in CI-based ASR and speech enhancement, among other related aspects.
The review will delve into potential applications and suggest future directions to bridge existing research gaps in this domain.
arXiv Detail & Related papers (2024-03-17T11:28:23Z) - Show from Tell: Audio-Visual Modelling in Clinical Settings [58.88175583465277]
We consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations without human expert annotation.
A simple yet effective multi-modal self-supervised learning framework is proposed for this purpose.
The proposed approach is able to localise anatomical regions of interest during ultrasound imaging, with only speech audio as a reference.
arXiv Detail & Related papers (2023-10-25T08:55:48Z) - Multi-class versus One-class classifier in spontaneous speech analysis
oriented to Alzheimer Disease diagnosis [58.720142291102135]
The aim of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from speech signal.
The use of information about outlier and Fractal Dimension features improves the system performance.
arXiv Detail & Related papers (2022-03-21T09:57:20Z) - Artificial Intelligence-Based Detection, Classification and
Prediction/Prognosis in PET Imaging: Towards Radiophenomics [2.2509387878255818]
This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging.
There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches.
Radiomics analysis has the potential to be utilized as a noninvasive technique for the accurate characterization of tumors.
arXiv Detail & Related papers (2021-10-20T01:05:47Z) - Discriminative Singular Spectrum Classifier with Applications on
Bioacoustic Signal Recognition [67.4171845020675]
We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently.
Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces.
The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
arXiv Detail & Related papers (2021-03-18T11:01:21Z) - Improving Medical Image Classification with Label Noise Using
Dual-uncertainty Estimation [72.0276067144762]
We discuss and define the two common types of label noise in medical images.
We propose an uncertainty estimation-based framework to handle these two label noise amid the medical image classification task.
arXiv Detail & Related papers (2021-02-28T14:56:45Z) - Medical Instrument Detection in Ultrasound-Guided Interventions: A
Review [74.22397862400177]
This article reviews medical instrument detection methods in the ultrasound-guided intervention.
First, we present a comprehensive review of instrument detection methodologies, which include traditional non-data-driven methods and data-driven methods.
We discuss the main clinical applications of medical instrument detection in ultrasound, including anesthesia, biopsy, prostate brachytherapy, and cardiac catheterization.
arXiv Detail & Related papers (2020-07-09T13:50:18Z) - Weakly supervised multiple instance learning histopathological tumor
segmentation [51.085268272912415]
We propose a weakly supervised framework for whole slide imaging segmentation.
We exploit a multiple instance learning scheme for training models.
The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
arXiv Detail & Related papers (2020-04-10T13:12:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.