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

Cerebral correlates and statistical criteria of cross-modal face and voice integration

Perception of faces and voices plays a prominent role in human social interaction, making multisensory integration of cross-modal speech a topic of great interest in cognitive neuroscience. How to define po- tential sites of multisensory integration using functional magnetic resonance imaging (fMRI) is currently under debate, with three statistical criteria frequently used (e.g., super-additive, max and mean criteria). In the present fMRI study, 20 participants were scanned in a block design under three stimulus conditions: dynamic unimodal face, unimodal voice and bimodal face–voice. Using this single dataset, we examine all these statistical criteria in an attempt to define loci of face–voice integration. While the super-additive and mean criteria essentially revealed regions in which one of the unimodal responses was a deactivation, the max criterion appeared stringent and only highlighted the left hippocampus as a potential site of face– voice integration. Psychophysiological interaction analysis showed that connectivity between occipital and temporal cortices increased during bimodal compared to unimodal conditions. We concluded that, when investigating multisensory integration with fMRI, all these criteria should be used in conjunction with ma- nipulation of stimulus signal-to-noise ratio and/or cross-modal congruency

Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances. In order to fuse the two modalities, we make use of a linear feature-wise fusion module that carries out an affine transformation on visual and auditory features. Finally, we conduct extensive experiments using the proposed dataset and approach. Experimental results show that introducing auditory information can benefit crowd counting under different illumination, noise, and occlusion conditions. The dataset and code will be released. Code and data have been made availabl

End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models

Speech activity detection (SAD) plays an important role in current speech processing systems, including automatic speech recognition (ASR). SAD is particularly difficult in environments with acoustic noise. A practical solution is to incorporate visual information, increasing the robustness of the SAD approach. An audiovisual system has the advantage of being robust to different speech modes (e.g., whisper speech) or background noise. Recent advances in audiovisual speech processing using deep learning have opened opportunities to capture in a principled way the temporal relationships between acoustic and visual features. This study explores this idea proposing a \emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach models the temporal dynamic of the sequential audiovisual data, improving the accuracy and robustness of the proposed SAD system. Instead of estimating hand-crafted features, the study investigates an end-to-end training approach, where acoustic and visual features are directly learned from the raw data during training. The experimental evaluation considers a large audiovisual corpus with over 60.8 hours of recordings, collected from 105 speakers. The results demonstrate that the proposed framework leads to absolute improvements up to 1.2% under practical scenarios over a VAD baseline using only audio implemented with deep neural network (DNN). The proposed approach achieves 92.7% F1-score when it is evaluated using the sensors from a portable tablet under noisy acoustic environment, which is only 1.0% lower than the performance obtained under ideal conditions (e.g., clean speech obtained with a high definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio

Voicing classification of visual speech using convolutional neural networks

The application of neural network and convolutional neural net- work (CNN) architectures is explored for the tasks of voicing classification (classifying frames as being either non-speech, unvoiced, or voiced) and voice activity detection (VAD) of vi- sual speech. Experiments are conducted for both speaker de- pendent and speaker independent scenarios. A Gaussian mixture model (GMM) baseline system is de- veloped using standard image-based two-dimensional discrete cosine transform (2D-DCT) visual speech features, achieving speaker dependent accuracies of 79% and 94%, for voicing classification and VAD respectively. Additionally, a single- layer neural network system trained using the same visual fea- tures achieves accuracies of 86 % and 97 %. A novel technique using convolutional neural networks for visual speech feature extraction and classification is presented. The voicing classifi- cation and VAD results using the system are further improved to 88 % and 98 % respectively. The speaker independent results show the neural network system to outperform both the GMM and CNN systems, achiev- ing accuracies of 63 % for voicing classification, and 79 % for voice activity detection

Dissociating task difficulty from incongruence in face-voice emotion integration

In the everyday environment, affective information is conveyed by both the face and the voice. Studies have demonstrated that a concurrently presented voice can alter the way that an emotional face expression is perceived, and vice versa, leading to emotional conflict if the information in the two modalities is mismatched. Additionally, evidence suggests that incongruence of emotional valence activates cerebral networks involved in conflict monitoring and resolution. However, it is currently unclear whether this is due to task difficulty—that incongruent stimuli are harder to categorize—or simply to the detection of mismatching information in the two modalities. The aim of the present fMRI study was to examine the neurophysiological correlates of processing incongruent emotional information, independent of task difficulty. Subjects were scanned while judging the emotion of face-voice affective stimuli. Both the face and voice were parametrically morphed between anger and happiness and then paired in all audiovisual combinations, resulting in stimuli each defined by two separate values: the degree of incongruence between the face and voice, and the degree of clarity of the combined face-voice information. Due to the specific morphing procedure utilized, we hypothesized that the clarity value, rather than incongruence value, would better reflect task difficulty. Behavioral data revealed that participants integrated face and voice affective information, and that the clarity, as opposed to incongruence value correlated with categorization difficulty. Cerebrally, incongruence was more associated with activity in the superior temporal region, which emerged after task difficulty had been accounted for. Overall, our results suggest that activation in the superior temporal region in response to incongruent information cannot be explained simply by task difficulty, and may rather be due to detection of mismatching information between the two modalities

Taking Synchrony Seriously: A Perceptual-Level Model of Infant Synchrony Detection

Synchrony detection between different sensory and/or motor channels appears critically important for young infant learning and cognitive development. For example, empirical studies demonstrate that audio-visual synchrony aids in language acquisition. In this paper we compare these infant studies with a model of synchrony detection based on the Hershey and Movellan (2000) algorithm augmented with methods for quantitative synchrony estimation. Four infant-model comparisons are presented, using audio-visual stimuli of increasing complexity. While infants and the model showed learning or discrimination with each type of stimuli used, the model was most successful with stimuli comprised of one audio and one visual source, and also with two audio sources and a dynamic-face visual motion source. More difficult for the model were stimuli conditions with two motion sources, and more abstract visual dynamics—an oscilloscope instead of a face. Future research should model the developmental pathway of synchrony detection. Normal audio-visual synchrony detection in infants may be experience-dependent (e.g., Bergeson, et al., 2004)

Speech-based recognition of self-reported and observed emotion in a dimensional space

The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance