arXiv subjects

Yoori Oh

Publications and source records attributed to Yoori Oh.

8 recordsLinked to original sources

arXiv2026

TripleSumm: Adaptive Triple-Modality Fusion for Video Summarization

The exponential growth of video content necessitates effective video summarization to efficiently extract key information from long videos. However, current approaches struggle to fully comprehend complex videos, primarily because they employ static or modality-agnostic fusion strategies. These methods fail to account for the dynamic, frame-dependent variations in modality saliency inherent in video data. To overcome these limitations, we propose TripleSumm, a novel architecture that adaptively weights and fuses the contributions of visual, text, and audio modalities at the frame level. Furthermore, a significant bottleneck for research into multimodal video summarization has been the lack of comprehensive benchmarks. Addressing this bottleneck, we introduce MoSu (Most Replayed Multimodal Video Summarization), the first large-scale benchmark that provides all three modalities. Extensive experiments demonstrate that TripleSumm achieves state-of-the-art performance, outperforming existing methods by a significant margin on four benchmarks, including MoSu. Our code and dataset are available at https://github.com/smkim37/TripleSumm.

cs.CV↗

arXiv2026

Sounding Highlights: Dual-Pathway Audio Encoders for Audio-Visual Video Highlight Detection

Audio-visual video highlight detection aims to automatically identify the most salient moments in videos by leveraging both visual and auditory cues. However, existing models often underutilize the audio modality, focusing on high-level semantic features while failing to fully leverage the rich, dynamic characteristics of sound. To address this limitation, we propose a novel framework, Dual-Pathway Audio Encoders for Video Highlight Detection (DAViHD). The dual-pathway audio encoder is composed of a semantic pathway for content understanding and a dynamic pathway that captures spectro-temporal dynamics. The semantic pathway extracts high-level information by identifying the content within the audio, such as speech, music, or specific sound events. The dynamic pathway employs a frequency-adaptive mechanism as time evolves to jointly model these dynamics, enabling it to identify transient acoustic events via salient spectral bands and rapid energy changes. We integrate the novel audio encoder into a full audio-visual framework and achieve new state-of-the-art performance on the large-scale MrHiSum benchmark. Our results demonstrate that a sophisticated, dual-faceted audio representation is key to advancing the field of highlight detection.

eess.AS↗

arXiv2026

Speaking Without Sound: Multi-speaker Silent Speech Voicing with Facial Inputs Only

In this paper, we introduce a novel framework for generating multi-speaker speech without relying on any audible inputs. Our approach leverages silent electromyography (EMG) signals to capture linguistic content, while facial images are used to match with the vocal identity of the target speaker. Notably, we present a pitch-disentangled content embedding that enhances the extraction of linguistic content from EMG signals. Extensive analysis demonstrates that our method can generate multi-speaker speech without any audible inputs and confirms the effectiveness of the proposed pitch-disentanglement approach.

cs.SD↗

arXiv2026

LipSody: Lip-to-Speech Synthesis with Enhanced Prosody Consistency

Lip-to-speech synthesis aims to generate speech audio directly from silent facial video by reconstructing linguistic content from lip movements, providing valuable applications in situations where audio signals are unavailable or degraded. While recent diffusion-based models such as LipVoicer have demonstrated impressive performance in reconstructing linguistic content, they often lack prosodic consistency. In this work, we propose LipSody, a lip-to-speech framework enhanced for prosody consistency. LipSody introduces a prosody-guiding strategy that leverages three complementary cues: speaker identity extracted from facial images, linguistic content derived from lip movements, and emotional context inferred from face video. Experimental results demonstrate that LipSody substantially improves prosody-related metrics, including global and local pitch deviations, energy consistency, and speaker similarity, compared to prior approaches.

cs.SD↗

arXiv2024

Hear Your Face: Face-based voice conversion with F0 estimation

This paper delves into the emerging field of face-based voice conversion, leveraging the unique relationship between an individual's facial features and their vocal characteristics. We present a novel face-based voice conversion framework that particularly utilizes the average fundamental frequency of the target speaker, derived solely from their facial images. Through extensive analysis, our framework demonstrates superior speech generation quality and the ability to align facial features with voice characteristics, including tracking of the target speaker's fundamental frequency.

cs.SD↗

arXiv2024

Distance Sampling-based Paraphraser Leveraging ChatGPT for Text Data Manipulation

There has been growing interest in audio-language retrieval research, where the objective is to establish the correlation between audio and text modalities. However, most audio-text paired datasets often lack rich expression of the text data compared to the audio samples. One of the significant challenges facing audio-text datasets is the presence of similar or identical captions despite different audio samples. Therefore, under many-to-one mapping conditions, audio-text datasets lead to poor performance of retrieval tasks. In this paper, we propose a novel approach to tackle the data imbalance problem in audio-language retrieval task. To overcome the limitation, we introduce a method that employs a distance sampling-based paraphraser leveraging ChatGPT, utilizing distance function to generate a controllable distribution of manipulated text data. For a set of sentences with the same context, the distance is used to calculate a degree of manipulation for any two sentences, and ChatGPT's few-shot prompting is performed using a text cluster with a similar distance defined by the Jaccard similarity. Therefore, ChatGPT, when applied to few-shot prompting with text clusters, can adjust the diversity of the manipulated text based on the distance. The proposed approach is shown to significantly enhance performance in audio-text retrieval, outperforming conventional text augmentation techniques.

cs.IR↗

arXiv2023

Semi-supervised learning for continuous emotional intensity controllable speech synthesis with disentangled representations

Recent text-to-speech models have reached the level of generating natural speech similar to what humans say. But there still have limitations in terms of expressiveness. The existing emotional speech synthesis models have shown controllability using interpolated features with scaling parameters in emotional latent space. However, the emotional latent space generated from the existing models is difficult to control the continuous emotional intensity because of the entanglement of features like emotions, speakers, etc. In this paper, we propose a novel method to control the continuous intensity of emotions using semi-supervised learning. The model learns emotions of intermediate intensity using pseudo-labels generated from phoneme-level sequences of speech information. An embedding space built from the proposed model satisfies the uniform grid geometry with an emotional basis. The experimental results showed that the proposed method was superior in controllability and naturalness.

eess.AS↗

arXiv2023

Exploring Train and Test-Time Augmentations for Audio-Language Learning

In this paper, we aim to unveil the impact of data augmentation in audio-language multi-modal learning, which has not been explored despite its importance. We explore various augmentation methods at not only train-time but also test-time and find out that proper data augmentation can lead to substantial improvements. Specifically, applying our proposed audio-language paired augmentation PairMix, which is the first multi-modal audio-language augmentation method, outperforms the baselines for both automated audio captioning and audio-text retrieval tasks. To fully take advantage of data augmentation, we also present multi-level test-time augmentation (Multi-TTA) for the test-time. We successfully incorporate the two proposed methods and uni-modal augmentations and achieve 47.5 SPIDEr on audio captioning, which is an 18.2% relative increase over the baseline. In audio-text retrieval, the proposed methods also show an improvement in performance as well.

cs.SD↗