SynesLM: A Unified Approach for Audio-visual Speech Recognition and Translation via Language Model and Synthetic Data

• URL: http://arxiv.org/abs/2408.00624v1
• Date: Thu, 1 Aug 2024 15:09:32 GMT
• Title: SynesLM: A Unified Approach for Audio-visual Speech Recognition and Translation via Language Model and Synthetic Data
• Authors: Yichen Lu, Jiaqi Song, Xuankai Chang, Hengwei Bian, Soumi Maiti, Shinji Watanabe,
• Abstract summary: We present SynesLM, a unified model which can perform three multimodal language understanding tasks. For zero-shot AV-ASR, SynesLM achieved SOTA performance by lowering the Word Error Rate (WER) from 43.4% to 39.4%. Our results in VST and VMT outperform the previous results, improving the BLEU score to 43.5 from 37.2 for VST, and to 54.8 from 54.4 for VMT.
• Score: 42.48380346580101
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we present SynesLM, an unified model which can perform three multimodal language understanding tasks: audio-visual automatic speech recognition(AV-ASR) and visual-aided speech/machine translation(VST/VMT). Unlike previous research that focused on lip motion as visual cues for speech signals, our work explores more general visual information within entire frames, such as objects and actions. Additionally, we use synthetic image data to enhance the correlation between image and speech data. We benchmark SynesLM against the How2 dataset, demonstrating performance on par with state-of-the-art (SOTA) models dedicated to AV-ASR while maintaining our multitasking framework. Remarkably, for zero-shot AV-ASR, SynesLM achieved SOTA performance by lowering the Word Error Rate (WER) from 43.4% to 39.4% on the VisSpeech Dataset. Furthermore, our results in VST and VMT outperform the previous results, improving the BLEU score to 43.5 from 37.2 for VST, and to 54.8 from 54.4 for VMT.

Related papers

• CLIP-VAD: Exploiting Vision-Language Models for Voice Activity Detection [2.110168344647122]
  Voice Activity Detection (VAD) is the process of automatically determining whether a person is speaking and identifying the timing of their speech. We introduce a novel approach leveraging Contrastive Language-Image Pretraining (CLIP) models. Our approach outperforms several audio-visual methods despite its simplicity, and without requiring pre-training on extensive audio-visual datasets.
  arXiv  Detail & Related papers  (2024-10-18T14:43:34Z)
• Robust Audiovisual Speech Recognition Models with Mixture-of-Experts [67.75334989582709]
  We introduce EVA, leveraging the mixture-of-Experts for audioVisual ASR to perform robust speech recognition for in-the-wild'' videos. We first encode visual information into visual tokens sequence and map them into speech space by a lightweight projection. Experiments show our model achieves state-of-the-art results on three benchmarks.
  arXiv  Detail & Related papers  (2024-09-19T00:08:28Z)
• Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer [59.57249127943914]
  We present a multilingual Audio-Visual Speech Recognition model incorporating several enhancements to improve performance and audio noise robustness. We increase the amount of audio-visual training data for six distinct languages, generating automatic transcriptions of unlabelled multilingual datasets. Our proposed model achieves new state-of-the-art performance on the LRS3 dataset, reaching WER of 0.8%.
  arXiv  Detail & Related papers  (2024-03-14T01:16:32Z)
• SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision [60.54020550732634]
  We study the potential of leveraging synthetic visual data for visual speech recognition (VSR) Key idea is to leverage a speech-driven lip animation model that generates lip movements conditioned on the input speech. We evaluate the performance of our approach on the largest public VSR benchmark - Lip Reading Sentences 3 (LRS3)
  arXiv  Detail & Related papers  (2023-03-30T07:43:27Z)
• AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR [79.21857972093332]
  We present AVFormer, a method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively.
  arXiv  Detail & Related papers  (2023-03-29T07:24:28Z)
• VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning [119.49605266839053]
  We propose a unified cross-modal representation learning framework VATLM (Visual-Audio-Text Language Model) The proposed VATLM employs a unified backbone network to model the modality-independent information. In order to integrate these three modalities into one shared semantic space, VATLM is optimized with a masked prediction task of unified tokens.
  arXiv  Detail & Related papers  (2022-11-21T09:10:10Z)
• Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction [26.27172574676212]
  Video recordings of speech contain correlated audio and visual information. We introduce Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech. AV-HuBERT learns powerful audio-visual speech representation benefiting both lip-reading and automatic speech recognition.
  arXiv  Detail & Related papers  (2022-01-05T17:40:45Z)
• Multiresolution and Multimodal Speech Recognition with Transformers [22.995102995029576]
  This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture. We focus on the scene context provided by the visual information, to ground the ASR. Our results are comparable to state-of-the-art Listen, Attend and Spell-based architectures.
  arXiv  Detail & Related papers  (2020-04-29T09:32:11Z)

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.