Measuring Sound Symbolism in Audio-visual Models

• URL: http://arxiv.org/abs/2409.12306v3
• Date: Mon, 11 Nov 2024 21:04:35 GMT
• Title: Measuring Sound Symbolism in Audio-visual Models
• Authors: Wei-Cheng Tseng, Yi-Jen Shih, David Harwath, Raymond Mooney,
• Abstract summary: This study investigates whether pre-trained audio-visual models demonstrate associations between sounds and visual representations. Our findings reveal connections to human language processing, providing insights in cognitive architectures and machine learning strategies.
• Score: 21.876743976994614
• License:
• Abstract: Audio-visual pre-trained models have gained substantial attention recently and demonstrated superior performance on various audio-visual tasks. This study investigates whether pre-trained audio-visual models demonstrate non-arbitrary associations between sounds and visual representations$\unicode{x2013}$known as sound symbolism$\unicode{x2013}$which is also observed in humans. We developed a specialized dataset with synthesized images and audio samples and assessed these models using a non-parametric approach in a zero-shot setting. Our findings reveal a significant correlation between the models' outputs and established patterns of sound symbolism, particularly in models trained on speech data. These results suggest that such models can capture sound-meaning connections akin to human language processing, providing insights into both cognitive architectures and machine learning strategies.

Related papers

• Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning [55.2480439325792]
  Large audio-language models (LALMs) have shown impressive capabilities in understanding and reasoning about audio and speech information. These models still face challenges, including hallucinating non-existent sound events, misidentifying the order of sound events, and incorrectly attributing sound sources.
  arXiv  Detail & Related papers  (2024-10-21T15:55:27Z)
• Developing Acoustic Models for Automatic Speech Recognition in Swedish [6.5458610824731664]
  This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish.
  arXiv  Detail & Related papers  (2024-04-25T12:03:14Z)
• AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models [92.92233932921741]
  We propose the AV-SUPERB benchmark that enables general-purpose evaluation of unimodal audio/visual and bimodal fusion representations. We evaluate 5 recent self-supervised models and show that none of these models generalize to all tasks. We show that representations may be improved with intermediate-task fine-tuning and audio event classification with AudioSet serves as a strong intermediate task.
  arXiv  Detail & Related papers  (2023-09-19T17:35:16Z)
• Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research [62.997667081978825]
  Modelling of early language acquisition aims to understand how infants bootstrap their language skills. Recent developments have enabled the use of more naturalistic training data for computational models. It is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data.
  arXiv  Detail & Related papers  (2023-05-03T08:25:37Z)
• Towards Disentangled Speech Representations [65.7834494783044]
  We construct a representation learning task based on joint modeling of ASR and TTS. We seek to learn a representation of audio that disentangles that part of the speech signal that is relevant to transcription from that part which is not. We show that enforcing these properties during training improves WER by 24.5% relative on average for our joint modeling task.
  arXiv  Detail & Related papers  (2022-08-28T10:03:55Z)
• Self-supervised models of audio effectively explain human cortical responses to speech [71.57870452667369]
  We capitalize on the progress of self-supervised speech representation learning to create new state-of-the-art models of the human auditory system. We show that these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
  arXiv  Detail & Related papers  (2022-05-27T22:04:02Z)
• Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data [24.608764078208953]
  Subgraphs are constructed by sampling the entire pool of available training data to exploit the relationship between labelled and unlabeled audio samples. We evaluate our model on three benchmark audio databases, and two tasks: acoustic event detection and speech emotion recognition. Our model is compact (240k parameters), and can produce generalized audio representations that are robust to different types of signal noise.
  arXiv  Detail & Related papers  (2022-01-31T21:32:22Z)
• Audiovisual Saliency Prediction in Uncategorized Video Sequences based on Audio-Video Correlation [0.0]
  This work aims to provide a generic audio/video saliency model augmenting a visual saliency map with an audio saliency map computed by synchronizing low-level audio and visual features. The proposed model was evaluated using different criteria against eye fixations data for a publicly available DIEM video dataset.
  arXiv  Detail & Related papers  (2021-01-07T14:22:29Z)
• COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations [32.456824945999465]
  We propose a method for learning audio representations, aligning the learned latent representations of audio and associated tags. We evaluate the quality of our embedding model, measuring its performance as a feature extractor on three different tasks.
  arXiv  Detail & Related papers  (2020-06-15T13:17:18Z)
• Curriculum Audiovisual Learning [113.20920928789867]
  We present a flexible audiovisual model that introduces a soft-clustering module as the audio and visual content detector. To ease the difficulty of audiovisual learning, we propose a novel learning strategy that trains the model from simple to complex scene. We show that our localization model significantly outperforms existing methods, based on which we show comparable performance in sound separation without referring external visual supervision.
  arXiv  Detail & Related papers  (2020-01-26T07:08:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.