Search Results for author:
Found 13 papers, 12 papers with code
End-to-end Training For Financial Report Summarization
Quoted companies are requested to periodically publish financial reports in textual form.
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.
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.
"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.
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
Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech
One head is specialized for diadochokinetic patterns.
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.
FlanEC: Exploring Flan-T5 for Post-ASR Error Correction
In this paper, we present an encoder-decoder model leveraging Flan-T5 for post-Automatic Speech Recognition (ASR) Generative Speech Error Correction (GenSEC), and we refer to it as FlanEC.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions
This work is concerned with devising a robust Parkinson's (PD) disease detector from speech in real-world operating conditions using (i) foundational models, and (ii) speech enhancement (SE) methods.
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.
An Investigation of Incorporating Mamba for Speech Enhancement
This work aims to study a scalable state-space model (SSM), Mamba, for the speech enhancement (SE) task.
Ranked #4 on
Speech Enhancement
on VoiceBank + DEMAND
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.
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.
