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Found 12 papers, 5 papers with code
Speak: A Toolkit Using Amazon Mechanical Turk to Collect and Validate Speech Audio Recordings
We present Speak, a toolkit that allows researchers to crowdsource speech audio recordings using Amazon Mechanical Turk (MTurk).
The Broad Impact of Feature Imitation: Neural Enhancements Across Financial, Speech, and Physiological Domains
Three different experiments are conducted in this study to test the applicability of imitating Tsallis entropy for performance enhancement: Bitcoin price prediction, speech emotion recognition, and chronic neck pain detection.
Feature Imitating Networks Enhance The Performance, Reliability And Speed Of Deep Learning On Biomedical Image Processing Tasks
Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e. g. Entropy), and then embedded into other networks to enhance their performance.
Perception, performance, and detectability of conversational artificial intelligence across 32 university courses
Moreover, current AI-text classifiers cannot reliably detect ChatGPT's use in school work, due to their propensity to classify human-written answers as AI-generated, as well as the ease with which AI-generated text can be edited to evade detection.
MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs
In this paper, we present Mambanet: a hybrid neural network for predicting the outcomes of Basketball games.
Nightly Automobile Claims Prediction from Telematics-Derived Features: A Multilevel Approach
This data is continuously collected and processed nightly into metadata consisting of mileage and time summaries of each discrete trip taken, and a set of behavioral scores describing attributes of the trip (e. g, driver fatigue or driver distraction) so we examine whether it can be used to identify periods of increased risk by successfully classifying trips that occur immediately before a trip in which there was an incident leading to a claim for that driver.
Fetal Gender Identification using Machine and Deep Learning Algorithms on Phonocardiogram Signals
In this work, we apply common PCG signal processing techniques on the gender-tagged Shiraz University Fetal Heart Sounds Database and study the applicability of previously proposed features in classifying fetal gender using both Machine Learning and Deep Learning models.
Feature Imitating Networks
In this paper, we introduce a novel approach to neural learning: the Feature-Imitating-Network (FIN).
SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations
This paper introduces SupCL-Seq, which extends the supervised contrastive learning from computer vision to the optimization of sequence representations in NLP.
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media
Adverse Events (AE) are harmful events resulting from the use of medical products.
EEG Channel Interpolation Using Deep Encoder-decoder Netwoks
Our approach exhibited a minimum of ~15% improvement over contemporary approaches when tested on subjects and tasks not used during model training.
Spoken Language Biomarkers for Detecting Cognitive Impairment
In this study we developed an automated system that evaluates speech and language features from audio recordings of neuropsychological examinations of 92 subjects in the Framingham Heart Study.
