2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
Cross-lingual Retrieval-Augmented Classification for Dysarthria Severity Assessment
Taeyoung Jeong, Insung Lee, Du-Seong Chang +1
Automatic dysarthria severity assessment is limited by the scarcity of labeled pathological speech data. To address this, we propose Cross-lingual Retrieval-Augmented Classificatio…
Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
HaeJun Yoo, Yongseop Shin, Insung Lee +2
Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-…
CAF-Score: Calibrating CLAP with LALMs for Reference-free Audio Captioning Evaluation
Insung Lee, Taeyoung Jeong, Haejun Yoo +2
While Large Audio-Language Models (LALMs) have advanced audio captioning, robust evaluation remains difficult. Reference-based metrics are expensive and often fail to assess acoust…
Speech Recognition-based Feature Extraction for Enhanced Automatic Severity Classification in Dysarthric Speech
Yerin Choi, Jeehyun Lee, Myoung-Wan Koo
Due to the subjective nature of current clinical evaluation, the need for automatic severity evaluation in dysarthric speech has emerged. DNN models outperform ML models but lack u…
DC CoMix TTS: An End-to-End Expressive TTS with Discrete Code Collaborated with Mixer
Yerin Choi, Myoung-Wan Koo
Despite the huge successes made in neutral TTS, content-leakage remains a challenge. In this paper, we propose a new input representation and simple architecture to achieve improve…