Visual Speech Enhancement

When video is shot in noisy environment, the voice of a speaker seen in the video can be enhanced using the visible mouth movements, reducing background noise. While most existing methods use audio-only inputs, improved performance is obtained with our visual speech enhancement, based on an audio-visual neural network. We include in the training data videos to which we added the voice of the target speaker as background noise. Since the audio input is not sufficient to separate the voice of a speaker from his own voice, the trained model better exploits the visual input and generalizes well to different noise types. The proposed model outperforms prior audio visual methods on two public lipreading datasets. It is also the first to be demonstrated on a dataset not designed for lipreading, such as the weekly addresses of Barack Obama.Comment: Accepted to Interspeech 2018. Supplementary video: https://www.youtube.com/watch?v=nyYarDGpcY

Investigating the Neural Basis of Audiovisual Speech Perception with Intracranial Recordings in Humans

Speech is inherently multisensory, containing auditory information from the voice and visual information from the mouth movements of the talker. Hearing the voice is usually sufficient to understand speech, however in noisy environments or when audition is impaired due to aging or disabilities, seeing mouth movements greatly improves speech perception. Although behavioral studies have well established this perceptual benefit, it is still not clear how the brain processes visual information from mouth movements to improve speech perception. To clarify this issue, I studied the neural activity recorded from the brain surfaces of human subjects using intracranial electrodes, a technique known as electrocorticography (ECoG). First, I studied responses to noisy speech in the auditory cortex, specifically in the superior temporal gyrus (STG). Previous studies identified the anterior parts of the STG as unisensory, responding only to auditory stimulus. On the other hand, posterior parts of the STG are known to be multisensory, responding to both auditory and visual stimuli, which makes it a key region for audiovisual speech perception. I examined how these different parts of the STG respond to clear versus noisy speech. I found that noisy speech decreased the amplitude and increased the across-trial variability of the response in the anterior STG. However, possibly due to its multisensory composition, posterior STG was not as sensitive to auditory noise as the anterior STG and responded similarly to clear and noisy speech. I also found that these two response patterns in the STG were separated by a sharp boundary demarcated by the posterior-most portion of the Heschl’s gyrus. Second, I studied responses to silent speech in the visual cortex. Previous studies demonstrated that visual cortex shows response enhancement when the auditory component of speech is noisy or absent, however it was not clear which regions of the visual cortex specifically show this response enhancement and whether this response enhancement is a result of top-down modulation from a higher region. To test this, I first mapped the receptive fields of different regions in the visual cortex and then measured their responses to visual (silent) and audiovisual speech stimuli. I found that visual regions that have central receptive fields show greater response enhancement to visual speech, possibly because these regions receive more visual information from mouth movements. I found similar response enhancement to visual speech in frontal cortex, specifically in the inferior frontal gyrus, premotor and dorsolateral prefrontal cortices, which have been implicated in speech reading in previous studies. I showed that these frontal regions display strong functional connectivity with visual regions that have central receptive fields during speech perception

Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems

Humans tend to change their way of speaking when they are immersed in a noisy environment, a reflex known as Lombard effect. Current speech enhancement systems based on deep learning do not usually take into account this change in the speaking style, because they are trained with neutral (non-Lombard) speech utterances recorded under quiet conditions to which noise is artificially added. In this paper, we investigate the effects that the Lombard reflex has on the performance of audio-visual speech enhancement systems based on deep learning. The results show that a gap in the performance of as much as approximately 5 dB between the systems trained on neutral speech and the ones trained on Lombard speech exists. This indicates the benefit of taking into account the mismatch between neutral and Lombard speech in the design of audio-visual speech enhancement systems

The Conversation: Deep Audio-Visual Speech Enhancement

Our goal is to isolate individual speakers from multi-talker simultaneous speech in videos. Existing works in this area have focussed on trying to separate utterances from known speakers in controlled environments. In this paper, we propose a deep audio-visual speech enhancement network that is able to separate a speaker's voice given lip regions in the corresponding video, by predicting both the magnitude and the phase of the target signal. The method is applicable to speakers unheard and unseen during training, and for unconstrained environments. We demonstrate strong quantitative and qualitative results, isolating extremely challenging real-world examples.Comment: To appear in Interspeech 2018. We provide supplementary material with interactive demonstrations on http://www.robots.ox.ac.uk/~vgg/demo/theconversatio

Multisensory Integration Sites Identified by Perception of Spatial Wavelet Filtered Visual Speech Gesture Information

Perception of speech is improved when presentation of the audio signal is accompanied by concordant visual speech gesture information. This enhancement is most prevalent when the audio signal is degraded. One potential means by which the brain affords perceptual enhancement is thought to be through the integration of concordant information from multiple sensory channels in a common site of convergence, multisensory integration (MSI) sites. Some studies have identified potential sites in the superior temporal gyrus/sulcus (STG/S) that are responsive to multisensory information from the auditory speech signal and visual speech movement. One limitation of these studies is that they do not control for activity resulting from attentional modulation cued by such things as visual information signaling the onsets and offsets of the acoustic speech signal, as well as activity resulting from MSI of properties of the auditory speech signal with aspects of gross visual motion that are not specific to place of articulation information. This fMRI experiment uses spatial wavelet bandpass filtered Japanese sentences presented with background multispeaker audio noise to discern brain activity reflecting MSI induced by auditory and visual correspondence of place of articulation information that controls for activity resulting from the above-mentioned factors. The experiment consists of a low-frequency (LF) filtered condition containing gross visual motion of the lips, jaw, and head without specific place of articulation information, a midfrequency (MF) filtered condition containing place of articulation information, and an unfiltered (UF) condition. Sites of MSI selectively induced by auditory and visual correspondence of place of articulation information were determined by the presence of activity for both the MF and UF conditions relative to the LF condition. Based on these criteria, sites of MSI were found predominantly in the left middle temporal gyrus (MTG), and the left STG/S (including the auditory cortex). By controlling for additional factors that could also induce greater activity resulting from visual motion information, this study identifies potential MSI sites that we believe are involved with improved speech perception intelligibility

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets and objective functions. In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets, given their central role in the development of data-driven approaches, and evaluation methods, because they are generally used to compare different systems and determine their performance