activity
20212026
collaborators

12 papers

cs.CL2026

DialFork: Going Beyond Factuality to Evaluate Procedural Hallucination in Conversational Agents

Sergio Burdisso, Esaú Villatoro-Tello, Dairazalia Sánchez-Cortés +4

Large language models (LLMs) deployed as conversational agents are increasingly expected to follow procedural logic. While factual hallucination has received extensive attention, a…

cs.CL2026

Benchmarking Automatic Speech Recognition Tools for Iberian Languages

Fernando López, Pablo Gómez, David Solans +2

Comprehensive evaluations of automatic speech recognition (ASR) for Iberian languages remain limited, and low-resource languages, biases, and efficiency trade-offs are underexplore…

cs.CL2026

The Eloquence submission for Task 2 of the Interspeech 2026 MLC-SLM challenge

Jordi Luque, Lorenzo Concina, Marco Matassoni +2

This paper details the Eloquence team's approach to Task 2 of the 2nd MLC-SLM challenge at Interspeech 2026, which involves multilingual Multiple-Choice Question Answering (MCQA) a…

cs.CL2026

ESCUCHA: A Spanish Speech Benchmark for Heterogeneous Acoustic Conditions

Fernando López, Ana Ayala, Guillermo Segovia +4

As large audio language models (LALMs) advance, robust evaluation frameworks have become essential. In this context, Spanish speech understanding under realistic acoustic condition…

cs.CL2026

S-DiverSe: Spanish Diverse Speech

Fernando López, Fernando Ibañez, Ana Martínez +4

Automatic speech recognition (ASR) has advanced remarkably for standard speech, yet speech affected by neurological conditions remains a challenge. We present S-DiverSe (Spanish Di…

cs.CL2026

FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition

Fernando López, Santosh Kesiraju, Jordi Luque

Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a significant challenge. We invest…

cs.CL2026

"OK Aura, Be Fair With Me": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection

Fernando López, Paula Delgado-Santos, Pablo Gómez +2

Voice-based interfaces are widely used; however, achieving fair Wake-up Word detection across diverse speaker populations remains a critical challenge due to persistent demographic…

cs.CL2025

Robustness assessment of large audio language models in multiple-choice evaluation

Fernando López, Santosh Kesiraju, Jordi Luque

Recent advances in large audio language models (LALMs) have primarily been assessed using a multiple-choice question answering (MCQA) framework. However, subtle changes, such as sh…

cs.SD2025

The Eloquence team submission for task 1 of MLC-SLM challenge

Lorenzo Concina, Jordi Luque, Alessio Brutti +2

In this paper, we present our studies and experiments carried out for the task 1 of the Challenge and Workshop on Multilingual Conversational Speech Language Model (MLC-SLM), which…

cs.CL2024

Word Sense Disambiguation in Native Spanish: A Comprehensive Lexical Evaluation Resource

Pablo Ortega, Jordi Luque, Luis Lamiable +2

Human language, while aimed at conveying meaning, inherently carries ambiguity. It poses challenges for speech and language processing, but also serves crucial communicative functi…

cs.SD2023

Robust Wake-Up Word Detection by Two-stage Multi-resolution Ensembles

Fernando López, Jordi Luque, Carlos Segura +1

Voice-based interfaces rely on a wake-up word mechanism to initiate communication with devices. However, achieving a robust, energy-efficient, and fast detection remains a challeng…

eess.AS2021

Speech Enhancement for Wake-Up-Word detection in Voice Assistants

David Bonet, Guillermo Cámbara, Fernando López +3

Keyword spotting and in particular Wake-Up-Word (WUW) detection is a very important task for voice assistants. A very common issue of voice assistants is that they get easily activ…