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20212026
most citedInterpretable Features for the Assessment of Neurodegenerative Diseases through Handwriting Analysis

1 citations · 1 across the 24 of their papers we have counts for

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eess.AS2025

ReFESS-QI: Reference-Free Evaluation For Speech Separation With Joint Quality And Intelligibility Scoring

Ari Frummer, Helin Wang, Tianyu Cao +6

Source separation is a crucial pre-processing step for various speech processing tasks, such as automatic speech recognition (ASR). Traditionally, the evaluation metrics for speech…

cs.CL2025

Where Do Backdoors Live? A Component-Level Analysis of Backdoor Propagation in Speech Language Models

Alexandrine Fortier, Thomas Thebaud, Jesús Villalba +3

Speech language models (SLMs) are systems of systems: independent components that unite to achieve a common goal. Despite their heterogeneous nature, SLMs are often studied end-to-…

eess.AS2025

Rhythm Features for Speaker Identification

Nick Mehlman, Thomas Thebaud, Dani Byrd +1

While deep learning models have demonstrated robust performance in speaker recognition tasks, they primarily rely on low-level audio features learned empirically from spectrograms…

cs.SD2025

Vox-Profile: A Speech Foundation Model Benchmark for Characterizing Diverse Speaker Traits and Speech Properties

Tiantian Feng, Jihwan Lee, Anfeng Xu +9

We introduce Vox-Profile, a comprehensive benchmark to characterize rich speaker traits and speech properties using speech foundation models. Unlike prior work that targets a singl…

eess.AS2025

SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline

Helin Wang, Jiarui Hai, Dongchao Yang +7

Target Speech Extraction (TSE) aims to isolate a target speaker's voice from a mixture of multiple speakers by leveraging speaker-specific cues, typically provided as auxiliary aud…

cs.CL2025

Demographic Attributes Prediction from Speech Using WavLM Embeddings

Yuchen Yang, Thomas Thebaud, Najim Dehak

This paper introduces a general classifier based on WavLM features, to infer demographic characteristics, such as age, gender, native language, education, and country, from speech.…

cs.LG2025

Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings

Sarah Laouedj, Yuzhe Wang, Jesus Villalba +3

In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with…