Search Results for author:
Found 15 papers, 11 papers with code
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.
Exploring Generative Error Correction for Dysarthric Speech Recognition
Despite the remarkable progress in end-to-end Automatic Speech Recognition (ASR) engines, accurately transcribing dysarthric speech remains a major challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
"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.
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.
"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.
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.
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.
Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models
We identify subgroups of audio recordings based on combinations of these metadata and compute each subgroup's performance (e. g., Word Error Rate) and the difference in performance (''divergence'') w. r. t the overall population.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
MALTO at SemEval-2024 Task 6: Leveraging Synthetic Data for LLM Hallucination Detection
In Natural Language Generation (NLG), contemporary Large Language Models (LLMs) face several challenges, such as generating fluent yet inaccurate outputs and reliance on fluency-centric metrics.
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.
