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arXiv · 2406.01321

Sequence-to-Sequence Multi-Modal Speech In-Painting

Mahsa Kadkhodaei Elyaderani·Shahram Shirani
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Abstract

Speech in-painting is the task of regenerating missing audio contents using reliable context information. Despite various recent studies in multi-modal perception of audio in-painting, there is still a need for an effective infusion of visual and auditory information in speech in-painting. In this paper, we introduce a novel sequence-to-sequence model that leverages the visual information to in-paint audio signals via an encoder-decoder architecture. The encoder plays the role of a lip-reader for facial recordings and the decoder takes both encoder outputs as well as the distorted audio spectrograms to restore the original speech. Our model outperforms an audio-only speech in-painting model and has comparable results with a recent multi-modal speech in-painter in terms of speech quality and intelligibility metrics for distortions of 300 ms to 1500 ms duration, which proves the effectiveness of the introduced multi-modality in speech in-painting.

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BibTeXRIS

Mahsa Kadkhodaei Elyaderani, Shahram Shirani. 2024-06-03. Sequence-to-Sequence Multi-Modal Speech In-Painting. https://arxiv.org/abs/2406.01321

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