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Register a new userMy lips are concealed: Audio-visual speech enhancement through obstructions
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Added by
Joon Son Chung
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Our objective is an audio-visual model for separating a single speaker from a mixture of sounds such as other speakers and background noise. Moreover, we wish to hear the speaker even when the visual cues are temporarily absent due to occlusion. To this end we introduce a deep audio-visual speech enhancement network that is able to separate a speakers voice by conditioning on both the speakers lip movements and/or a representation of their voice. The voice representation can be obtained by either (i) enrollment, or (ii) by self-enrollment -- learning the representation on-the-fly given sufficient unobstructed visual input. The model is trained by blending audios, and by introducing artificial occlusions around the mouth region that prevent the visual modality from dominating. The method is speaker-independent, and we demonstrate it on real examples of speakers unheard (and unseen) during training. The method also improves over previous models in particular for cases of occlusion in the visual modality.
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Human speech processing is inherently multimodal, where visual cues (lip movements) help to better understand the speech in noise. Lip-reading driven speech enhancement significantly outperforms benchmark audio-only approaches at low signal-to-noise ratios (SNRs). However, at high SNRs or low levels of background noise, visual cues become fairly less effective for speech enhancement. Therefore, a more optimal, context-aware audio-visual (AV) system is required, that contextually utilises both visual and noisy audio features and effectively accounts for different noisy conditions. In this paper, we introduce a novel contextual AV switching component that contextually exploits AV cues with respect to different operating conditions to estimate clean audio, without requiring any SNR estimation. The switching module switches between visual-only (V-only), audio-only (A-only), and both AV cues at low, high and moderate SNR levels, respectively. The contextual AV switching component is developed by integrating a convolutional neural network and long-short-term memory network. For testing, the estimated clean audio features are utilised by the developed novel enhanced visually derived Wiener filter for clean audio power spectrum estimation. The contextual AV speech enhancement method is evaluated under real-world scenarios using benchmark Grid and ChiME3 corpora. For objective testing, perceptual evaluation of speech quality is used to evaluate the quality of the restored speech. For subjective testing, the standard mean-opinion-score method is used. The critical analysis and comparative study demonstrate the outperformance of proposed contextual AV approach, over A-only, V-only, spectral subtraction, and log-minimum mean square error based speech enhancement methods at both low and high SNRs, revealing its capability to tackle spectro-temporal variation in any real-world noisy condition.
We describe a system for large-scale audiovisual translation and dubbing, which translates videos from one language to another. The source languages speech content is transcribed to text, translated, and automatically synthesized into target language speech using the original speakers voice. The visual content is translated by synthesizing lip movements for the speaker to match the translated audio, creating a seamless audiovisual experience in the target language. The audio and visual translation subsystems each contain a large-scale generic synthesis model trained on thousands of hours of data in the corresponding domain. These generic models are fine-tuned to a specific speaker before translation, either using an auxiliary corpus of data from the target speaker, or using the video to be translated itself as the input to the fine-tuning process. This report gives an architectural overview of the full system, as well as an in-depth discussion of the video dubbing component. The role of the audio and text components in relation to the full system is outlined, but their design is not discussed in detail. Translated and dubbed demo videos generated using our system can be viewed at https://www.youtube.com/playlist?list=PLSi232j2ZA6_1Exhof5vndzyfbxAhhEs5
Variational auto-encoders (VAEs) are deep generative latent variable models that can be used for learning the distribution of complex data. VAEs have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. One advantage of this generative approach is that it does not require pairs of clean and noisy speech signals at training. In this paper, we propose audio-visual variants of VAEs for single-channel and speaker-independent speech enhancement. We develop a conditional VAE (CVAE) where the audio speech generative process is conditioned on visual information of the lip region. At test time, the audio-visual speech generative model is combined with a noise model based on nonnegative matrix factorization, and speech enhancement relies on a Monte Carlo expectation-maximization algorithm. Experiments are conducted with the recently published NTCD-TIMIT dataset as well as the GRID corpus. The results confirm that the proposed audio-visual CVAE effectively fuses audio and visual information, and it improves the speech enhancement performance compared with the audio-only VAE model, especially when the speech signal is highly corrupted by noise. We also show that the proposed unsupervised audio-visual speech enhancement approach outperforms a state-of-the-art supervised deep learning method.
Speech as a natural signal is composed of three parts - visemes (visual part of speech), phonemes (spoken part of speech), and language (the imposed structure). However, video as a medium for the delivery of speech and a multimedia construct has mostly ignored the cognitive aspects of speech delivery. For example, video applications like transcoding and compression have till now ignored the fact how speech is delivered and heard. To close the gap between speech understanding and multimedia video applications, in this paper, we show the initial experiments by modelling the perception on visual speech and showing its use case on video compression. On the other hand, in the visual speech recognition domain, existing studies have mostly modeled it as a classification problem, while ignoring the correlations between views, phonemes, visemes, and speech perception. This results in solutions which are further away from how human perception works. To bridge this gap, we propose a view-temporal attention mechanism to model both the view dependence and the visemic importance in speech recognition and understanding. We conduct experiments on three public visual speech recognition datasets. The experimental results show that our proposed method outperformed the existing work by 4.99% in terms of the viseme error rate. Moreover, we show that there is a strong correlation between our models understanding of multi-view speech and the human perception. This characteristic benefits downstream applications such as video compression and streaming where a significant number of less important frames can be compressed or eliminated while being able to maximally preserve human speech understanding with good user experience.
We introduce a new approach for audio-visual speech separation. Given a video, the goal is to extract the speech associated with a face in spite of simultaneous background sounds and/or other human speakers. Whereas existing methods focus on learning the alignment between the speakers lip movements and the sounds they generate, we propose to leverage the speakers face appearance as an additional prior to isolate the corresponding vocal qualities they are likely to produce. Our approach jointly learns audio-visual speech separation and cross-modal speaker embeddings from unlabeled video. It yields state-of-the-art results on five benchmark datasets for audio-visual speech separation and enhancement, and generalizes well to challenging real-world videos of diverse scenarios. Our video results and code: http://vision.cs.utexas.edu/projects/VisualVoice/.
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