paper-with-me

홈 › Papers

Are words equally surprising in audio and audio-visual comprehension?

2023-07-14 · Pranava Madhyastha, Ye Zhang, Gabriella Vigliocco

We report a controlled study investigating the effect of visual information (i.e., seeing the speaker) on spoken language comprehension. We compare the ERP signature (N400) associated with each word in audio-only and audio-visual presentations of the same verbal stimuli. We assess the extent to which surprisal measures (which quantify the predictability of words in their lexical context) are generated on the basis of different types of language models (specifically n-gram and Transformer models) that predict N400 responses for each word. Our results indicate that cognitive effort differs significantly between multimodal and unimodal settings. In addition, our findings suggest that while Transformer-based models, which have access to a larger lexical context, provide a better fit in the audio-only setting, 2-gram language models are more effective in the multimodal setting. This highlights the significant impact of local lexical context on cognitive processing in a multimodal environment.

📄 PDF Abstract BibTeX arXiv:2307.07277

Code (0)

등록된 구현이 없습니다.

Tasks

ERP

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음
Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…
Position-Wise Feed-Forward Layer 설명 없음
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Label Smoothing Label Smoothing is a regularization technique that introduces noise for the labels. This accounts for the fact that datasets may have mistakes in them, so maximizing the…
Residual Connection 설명 없음

Similar Papers 제목 키워드 기반

An Attempt towards Interpretable Audio-Visual Video Captioning

2018-12-07 · Yapeng Tian, Chenxiao Guan, Justin Goodman, Marc Moore 외

Automatically generating a natural language sentence to describe the content of an input video is a very challenging problem. It is an essential multimodal task in which auditory and visual contents are equally important…

Audio captioningAudio-Visual Video CaptioningImage CaptioningSentence+2

Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation

2025-03-17 · CVPR 2025 1 · Henghui Du, Guangyao Li, Chang Zhou, Chunjie Zhang 외

In recent years, numerous tasks have been proposed to encourage model to develop specified capability in understanding audio-visual scene, primarily categorized into temporal localization, spatial localization, spatio-te…

Data InteractionScene UnderstandingTemporal LocalizationUIE

Recognition of Isolated Words using Zernike and MFCC features for Audio Visual Speech Recognition

2014-07-04 · Prashant Bordea, Amarsinh Varpeb, Ramesh Manzac, Pravin Yannawara

Automatic Speech Recognition (ASR) by machine is an attractive research topic in signal processing domain and has attracted many researchers to contribute in this area. In recent year, there have been many advances in au…

Audio-Visual Speech RecognitionAutomatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognition+2

MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis

2024-12-19 · CVPR 2025 1 · Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya 외

We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. In contrast to single-modality training conditioned on (l…

Audio GenerationAudio SynthesisAudio-Visual SynchronizationVideo-to-Sound Generation

Multimodal Confidence Modeling in Audio-Visual Quality Assessment

2026-05-02 · Mayesha Maliha R. Mithila, Mylene C. Q. Farias arxiv

Audio-visual quality assessment (AVQA) is essential for streaming, teleconferencing, and immersive media. In realistic streaming scenarios, distortions are often asymmetric, where one modality may be severely degraded wh…