Uncovering the Visual Contribution in Audio-Visual Speech Recognition
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866908339168870400 |
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| author | Lin, Zhaofeng Harte, Naomi |
| author_facet | Lin, Zhaofeng Harte, Naomi |
| contents | Audio-Visual Speech Recognition (AVSR) combines auditory and visual speech cues to enhance the accuracy and robustness of speech recognition systems. Recent advancements in AVSR have improved performance in noisy environments compared to audio-only counterparts. However, the true extent of the visual contribution, and whether AVSR systems fully exploit the available cues in the visual domain, remains unclear. This paper assesses AVSR systems from a different perspective, by considering human speech perception. We use three systems: Auto-AVSR, AVEC and AV-RelScore. We first quantify the visual contribution using effective SNR gains at 0 dB and then investigate the use of visual information in terms of its temporal distribution and word-level informativeness. We show that low WER does not guarantee high SNR gains. Our results suggest that current methods do not fully exploit visual information, and we recommend future research to report effective SNR gains alongside WERs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17129 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncovering the Visual Contribution in Audio-Visual Speech Recognition Lin, Zhaofeng Harte, Naomi Audio and Speech Processing Sound Audio-Visual Speech Recognition (AVSR) combines auditory and visual speech cues to enhance the accuracy and robustness of speech recognition systems. Recent advancements in AVSR have improved performance in noisy environments compared to audio-only counterparts. However, the true extent of the visual contribution, and whether AVSR systems fully exploit the available cues in the visual domain, remains unclear. This paper assesses AVSR systems from a different perspective, by considering human speech perception. We use three systems: Auto-AVSR, AVEC and AV-RelScore. We first quantify the visual contribution using effective SNR gains at 0 dB and then investigate the use of visual information in terms of its temporal distribution and word-level informativeness. We show that low WER does not guarantee high SNR gains. Our results suggest that current methods do not fully exploit visual information, and we recommend future research to report effective SNR gains alongside WERs. |
| title | Uncovering the Visual Contribution in Audio-Visual Speech Recognition |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2412.17129 |