303 citations · 612 across the 62 of their papers we have counts for
63 papers
RT-SEMamba: Real-Time Speech Enhancement Mamba via Progressive Knowledge Distillation
Rong Chao, Sung-Feng Huang, Moreno La Quatra +4
We present RT-SEMamba, a fully causal speech enhancement (SE) model built upon causal time-frequency Mamba blocks. Unlike Transformer-based architectures that rely on a growing key…
Few-Shot and Pseudo-Label Guided Speech Quality Evaluation with Large Language Models
Ryandhimas E. Zezario, Dyah A. M. G. Wisnu, Szu-Wei Fu +3
In this paper, we introduce GatherMOS, a novel framework that leverages large language models (LLM) as meta-evaluators to aggregate diverse signals into quality predictions. Gather…
A Knowledge-Driven Approach to Target Speech Extraction in the Presence of Background Sound Effects for Cinematic Audio Source Separation (CASS)
Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li +1
We propose a knowledge-driven approach to speech target extraction in the presence of background sound effects already recorded in cinematic audio. The specific knowledge sources s…
A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation
Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li +1
We propose a knowledge-driven, model-based approach to segmenting audio into single-category and mixed-category chunks with applications to source separation. "Knowledge" here deno…
MDM-ASR: Bridging Accuracy and Efficiency in ASR with Diffusion-Based Non-Autoregressive Decoding
Hao Yen, Pin-Jui Ku, Ante Jukić +1
In sequence-to-sequence Transformer ASR, autoregressive (AR) models achieve strong accuracy but suffer from slow decoding, while non-autoregressive (NAR) models enable parallel dec…
Hallucination Benchmark for Speech Foundation Models
Alkis Koudounas, Moreno La Quatra, Manuel Giollo +2
Hallucinations in automatic speech recognition (ASR) systems refer to fluent and coherent transcriptions produced by neural ASR models that are completely unrelated to the underlyi…
HDA-SELD: Hierarchical Cross-Modal Distillation with Multi-Level Data Augmentation for Low-Resource Audio-Visual Sound Event Localization and Detection
Qing Wang, Ya Jiang, Hang Chen +3
This work presents HDA-SELD, a unified framework that combines hierarchical cross-modal distillation (HCMD) and multi-level data augmentation to address low-resource audio-visual (…
A Bottom-up Framework with Language-universal Speech Attribute Modeling for Syllable-based ASR
Hao Yen, Pin-Jui Ku, Sabato Marco Siniscalchi +1
We propose a bottom-up framework for automatic speech recognition (ASR) in syllable-based languages by unifying language-universal articulatory attribute modeling with syllable-lev…
TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
Tong Guan, Zijie Meng, Dianqi Li +7
Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. Ho…
Feature Importance across Domains for Improving Non-Intrusive Speech Intelligibility Prediction in Hearing Aids
Ryandhimas E. Zezario, Sabato M. Siniscalchi, Fei Chen +2
Given the critical role of non-intrusive speech intelligibility assessment in hearing aids (HA), this paper enhances its performance by introducing Feature Importance across Domain…
An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement
Chun-Wei Ho, Pin-Jui Ku, Hao Yen +3
We propose a novel iterative phase estimation framework, termed multi-source Griffin-Lim algorithm (MSGLA), for speech enhancement (SE) under additive noise conditions. The core id…
Aligning Generative Speech Enhancement with Perceptual Feedback
Haoyang Li, Nana Hou, Yuchen Hu +6
Language Model (LM)-based speech enhancement (SE) has recently emerged as a promising direction, but existing approaches predominantly rely on token-level likelihood objectives tha…
Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation
Jun Qi, Chen-Yu Liu, Sabato Marco Siniscalchi +2
Low-Rank Adaptation (LoRA) is widely recognized for its parameter-efficient fine-tuning of large-scale neural models. However, standard LoRA independently optimizes low-rank matric…
The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition
Ming Gao, Shilong Wu, Hang Chen +6
Meetings are a valuable yet challenging scenario for speech applications due to complex acoustic conditions. This paper summarizes the outcomes of the MISP 2025 Challenge, hosted a…
"KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding
Alkis Koudounas, Moreno La Quatra, Eliana Pastor +2
Kolmogorov-Arnold Networks (KANs) have recently emerged as a promising alternative to traditional neural architectures, yet their application to speech processing remains under exp…
Exploring Generative Error Correction for Dysarthric Speech Recognition
Moreno La Quatra, Alkis Koudounas, Valerio Mario Salerno +1
Despite the remarkable progress in end-to-end Automatic Speech Recognition (ASR) engines, accurately transcribing dysarthric speech remains a major challenge. In this work, we prop…
MVP: Multi-source Voice Pathology detection
Alkis Koudounas, Moreno La Quatra, Gabriele Ciravegna +6
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 t…
Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations
Whenty Ariyanti, Kuan-Yu Chen, Sabato Marco Siniscalchi +2
Perceptual voice quality assessment plays a vital role in diagnosing and monitoring voice disorders. Traditional methods, such as the Consensus Auditory-Perceptual Evaluation of Vo…
Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech
Moreno La Quatra, Juan Rafael Orozco-Arroyave, Marco Sabato Siniscalchi
This work aims to tackle the Parkinson's disease (PD) detection problem from the speech signal in a bilingual setting by proposing an ad-hoc dual-head deep neural architecture for…
Variational Bayesian Adaptive Learning of Deep Latent Variables for Acoustic Knowledge Transfer
Hu Hu, Sabato Marco Siniscalchi, Chao-Han Huck Yang +1
In this work, we propose a novel variational Bayesian adaptive learning approach for cross-domain knowledge transfer to address acoustic mismatches between training and testing con…
FlanEC: Exploring Flan-T5 for Post-ASR Error Correction
Moreno La Quatra, Valerio Mario Salerno, Yu Tsao +1
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…
From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology
Haoyang Li, Yuchen Hu, Chen Chen +3
Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures neede…
MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network
Yu-Tung Liu, Kuan-Chen Wang, Rong Chao +3
Surface electromyography (sEMG) recordings can be contaminated by electrocardiogram (ECG) signals when the monitored muscle is closed to the heart. Traditional signal processing-ba…
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Chao-Han Huck Yang, Taejin Park, Yuan Gong +18
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen,…