9 citations · 9 across the 4 of their papers we have counts for
5 papers
Domain-Incremental Learning for Generative Speech Enhancement
Manjunath Mulimani, Annamaria Mesaros, Minje Kim +1
We propose a domain-incremental learning framework for generative speech enhancement (SE) that learns from a sequence of datasets or domains recorded under diverse acoustic conditi…
Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech
Jaesung Bae, Xiuwen Zheng, Minje Kim +2
Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evaluation is costly and difficult to scale,…
Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement
Jae-Sung Bae, Anastasia Kuznetsova, Dinesh Manocha +3
This paper presents a new challenge that calls for zero-shot text-to-speech (TTS) systems to augment speech data for the downstream task, personalized speech enhancement (PSE), as…
Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation
Jackie Lin, Georg Götz, Hermes Sampedro Llopis +9
This paper describes the synthesis of the room acoustics challenge as a part of the generative data augmentation workshop at ICASSP 2025. The challenge defines a unique generative…
Neural Speech and Audio Coding: Modern AI Technology Meets Traditional Codecs
Minje Kim, Jan Skoglund
This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the…