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Found 23 papers, 17 papers with code
MVP: Multi-source Voice Pathology detection
Voice disorders significantly impact patient quality of life, yet non-invasive automated diagnosis remains under-explored due to both the scarcity of pathological voice data, and the variability in recording sources.
"KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding
Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional neural architectures, yet their application to speech processing remains under explored.
DeepDialogue: A Multi-Turn Emotionally-Rich Spoken Dialogue Dataset
Recent advances in conversational AI have demonstrated impressive capabilities in single-turn responses, yet multi-turn dialogues remain challenging for even the most sophisticated language models.
"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding
Machine unlearning, the process of efficiently removing specific information from machine learning models, is a growing area of interest for responsible AI.
voc2vec: A Foundation Model for Non-Verbal Vocalization
In this work, we aim to overcome the above shortcoming and propose a novel foundation model, termed voc2vec, specifically designed for non-verbal human data leveraging exclusively open-source non-verbal audio datasets.
A Synthetic Benchmark to Explore Limitations of Localized Drift Detections
Concept drift is a common phenomenon in data streams where the statistical properties of the target variable change over time.
Detecting Interpretable Subgroup Drifts
The ability to detect and adapt to changes in data distributions is crucial to maintain the accuracy and reliability of machine learning models.
KAN You See It? KANs and Sentinel for Effective and Explainable Crop Field Segmentation
This paper analyzes the integration of KAN layers into the U-Net architecture (U-KAN) to segment crop fields using Sentinel-2 and Sentinel-1 satellite images and provides an analysis of the performance and explainability of these networks.
Speech Analysis of Language Varieties in Italy
We leverage self-supervised learning models to tackle this task and analyze differences and similarities between Italy's regional languages.
A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data
Kolmogorov-Arnold Networks (KANs) have very recently been introduced into the world of machine learning, quickly capturing the attention of the entire community.
Voice Disorder Analysis: a Transformer-based Approach
Voice disorders are pathologies significantly affecting patient quality of life.
A Contrastive Learning Approach to Mitigate Bias in Speech Models
Speech models may be affected by performance imbalance in different population subgroups, raising concerns about fair treatment across these groups.
Benchmarking Representations for Speech, Music, and Acoustic Events
Limited diversity in standardized benchmarks for evaluating audio representation learning (ARL) methods may hinder systematic comparison of current methods' capabilities.
Concept-based Explainable Artificial Intelligence: A Survey
The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models.
Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding.
Explaining Speech Classification Models via Word-Level Audio Segments and Paralinguistic Features
Existing work focuses on a few spoken language understanding (SLU) tasks, and explanations are difficult to interpret for most users.
ITALIC: An Italian Intent Classification Dataset
Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects.
Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists
EAR also reveals overfitting terms, i. e., terms most likely to induce bias, to help identify their effect on the model, task, and predictions.
Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework.
Identifying Biased Subgroups in Ranking and Classification
Furthermore, we quantify the contribution of all attributes in the data subgroup to the divergent behavior by means of Shapley values, thus allowing the identification of the most impacting attributes.
Automating concept-drift detection by self-evaluating predictive model degradation
To this aim, suitable automatic solutions to self-assess the prediction quality and the data distribution drift between the original training set and the new data have to be devised.
Detecting Anomalies in Image Classification by Means of Semantic Relationships
This paper presents a semantic anomaly detection method (SAD) to detect anomalies in the predictions of any pixelwise semantic segmentation algorithm.
Scaling associative classification for very large datasets
DAC exploits ensemble learning to distribute the training of an associative classifier among parallel workers and improve the final quality of the model.
