Robust Multi-Modal Speech In-Painting: A Sequence-to-Sequence Approach

• URL: http://arxiv.org/abs/2406.00901v1
• Date: Sun, 2 Jun 2024 23:51:43 GMT
• Title: Robust Multi-Modal Speech In-Painting: A Sequence-to-Sequence Approach
• Authors: Mahsa Kadkhodaei Elyaderani, Shahram Shirani,
• Abstract summary: We introduce and study a sequence-to-sequence (seq2seq) speech in-painting model that incorporates AV features. Our approach extends AV speech in-painting techniques to scenarios where both audio and visual data may be jointly corrupted.
• Score: 3.89476785897726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The process of reconstructing missing parts of speech audio from context is called speech in-painting. Human perception of speech is inherently multi-modal, involving both audio and visual (AV) cues. In this paper, we introduce and study a sequence-to-sequence (seq2seq) speech in-painting model that incorporates AV features. Our approach extends AV speech in-painting techniques to scenarios where both audio and visual data may be jointly corrupted. To achieve this, we employ a multi-modal training paradigm that boosts the robustness of our model across various conditions involving acoustic and visual distortions. This makes our distortion-aware model a plausible solution for real-world challenging environments. We compare our method with existing transformer-based and recurrent neural network-based models, which attempt to reconstruct missing speech gaps ranging from a few milliseconds to over a second. Our experimental results demonstrate that our novel seq2seq architecture outperforms the state-of-the-art transformer solution by 38.8% in terms of enhancing speech quality and 7.14% in terms of improving speech intelligibility. We exploit a multi-task learning framework that simultaneously performs lip-reading (transcribing video components to text) while reconstructing missing parts of the associated speech.

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