5 papers
ELSA: Acoustic Event-Level Semantic Alignment for Fine-Grained Reference-Free Text-to-Audio Evaluation
Shuntaro Suzuki, Kento Tokura, Daichi Yashima +3
Text-to-audio (TTA) generation, synthesizing audio from natural language, has been widely studied for its ability to capture precise user intent. To effectively advance TTA models,…
ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
Daichi Yashima, Shuhei Kurita, Yusuke Oda +3
In this study, we focus on video captioning by fully open multimodal large language models (MLLMs). The comprehension of visual sequences is challenging because of their intricate…
MEGState: Phoneme Decoding from Magnetoencephalography Signals
Shuntaro Suzuki, Chia-Chun Dan Hsu, Yu Tsao +1
Decoding linguistically meaningful representations from non-invasive neural recordings remains a central challenge in neural speech decoding. Among available neuroimaging modalitie…
Condition-Invariant fMRI Decoding of Speech Intelligibility with Deep State Space Model
Ching-Chih Sung, Shuntaro Suzuki, Francis Pingfan Chien +2
Clarifying the neural basis of speech intelligibility is critical for computational neuroscience and digital speech processing. Recent neuroimaging studies have shown that intellig…
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura
Classification of electroencephalogram (EEG) signals obtained during motor imagery (MI) has substantial application potential, including communication assistance and rehabilitation…