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1. Expressive Speech-Driven Lip Movements with Multitask Learning

   https://doi.org/10.1109/FG.2018.00066  

   Sadoughi, Najmeh ; Busso, Carlos ( May 2018 , IEEE Conference on Automatic Face and Gesture Recognition (FG 2018))

   The orofacial area conveys a range of information, including speech articulation and emotions. These two factors add constraints to the facial movements, creating non-trivial integrations and interplays. To generate more expressive and naturalistic movements for conversational agents (CAs) the relationship between these factors should be carefully modeled. Data-driven models are more appropriate for this task than rule-based systems. This paper provides two deep learning speech-driven structures to integrate speech articulation and emotional cues. The proposed approaches rely on multitask learning (MTL) strategies, where related secondary tasks are jointly solved when synthesizing orofacial movements. In particular, we evaluate emotion recognition and viseme recognition as secondary tasks. The approach creates shared representations that generate behaviors that not only are closer to the original orofacial movements, but also are perceived more natural than the results from single task learning. 

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2. Using Co-Captured Face, Gaze, and Verbal Reactions to Images of Varying Emotional Content for Analysis and Semantic Alignment

   Gangji, Aliya ; Walden, Trevor ; Vaidyanathan, Preethi ; Prud'hommeaux, Emily ; Bailey, Reynold ; Alm, Cecilia O ( January 2017 , The AAAI-17 Workshop on Human-Aware Artificial Intelligence)

   Analyzing different modalities of expression can provide insights into the ways that humans interpret, label, and react to images. Such insights have the potential not only to advance our understanding of how humans coordinate these expressive modalities but also to enhance existing methodologies for common AI tasks such as image annotation and classification. We conducted an experiment that co-captured the facial expressions, eye movements, and spoken language data that observers produce while examining images of varying emotional content and responding to description-oriented vs. affect-oriented questions about those images. We analyzed the facial expressions produced by the observers in order to determine the connection between those expressions and an image's emotional content. We also explored the relationship between the valence of an image and the verbal responses to that image, and how that relationship relates to the nature of the prompt, using low-level lexical features and more complex affective features extracted from the observers' verbal responses. Finally, in order to integrate this multimodal data, we extended an existing bitext alignment framework to create meaningful pairings between narrated observations about images and the image regions indicated by eye movement data. The resulting annotations of image regions with words from observers' responses demonstrate the potential of bitext alignment for multimodal data integration and, from an application perspective, for annotation of open-domain images. In addition, we found that while responses to affect-oriented questions appear useful for image understanding, their holistic nature seems less helpful for image region annotation. 

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3. Speech-Driven Expressive Talking Lips with Conditional Sequential Generative Adversarial Networks

   https://doi.org/10.1109/TAFFC.2019.2916031  

   Sadoughi, Najmeh ; Busso, Carlos ( May 2019 , IEEE Transactions on Affective Computing)

   null (Ed.)

   Articulation, emotion, and personality play strong roles in the orofacial movements. To improve the naturalness and expressiveness of virtual agents(VAs), it is important that we carefully model the complex interplay between these factors. This paper proposes a conditional generative adversarial network, called conditional sequential GAN(CSG), which learns the relationship between emotion, lexical content and lip movements in a principled manner. This model uses a set of spectral and emotional speech features directly extracted from the speech signal as conditioning inputs, generating realistic movements. A key feature of the approach is that it is a speech-driven framework that does not require transcripts. Our experiments show the superiority of this model over three state-of-the-art baselines in terms of objective and subjective evaluations. When the target emotion is known, we propose to create emotionally dependent models by either adapting the base model with the target emotional data (CSG-Emo-Adapted), or adding emotional conditions as the input of the model(CSG-Emo-Aware). Objective evaluations of these models show improvements for the CSG-Emo-Adapted compared with the CSG model, as the trajectory sequences are closer to the original sequences. Subjective evaluations show significantly better results for this model compared with the CSG model when the target emotion is happiness. 

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4. Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks

   Sadoughi, Najmeh and ( January 2018 , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018))

   Head movement is an integral part of face-to-face communications. It is important to investigate methodologies to generate naturalistic movements for conversational agents (CAs). The predominant method for head movement generation is using rules based on the meaning of the message. However, the variations of head movements by these methods are bounded by the predefined dictionary of gestures. Speech-driven methods offer an alternative approach, learning the relationship between speech and head movements from real recordings. However, previous studies do not generate novel realizations for a repeated speech signal. Conditional generative adversarial network (GAN) provides a framework to generate multiple realizations of head movements for each speech segment by sampling from a conditioned distribution. We build a conditional GAN with bidirectional long-short term memory (BLSTM), which is suitable for capturing the long-short term dependencies of time- continuous signals. This model learns the distribution of head movements conditioned on speech prosodic features. We compare this model with a dynamic Bayesian network (DBN) and BLSTM models optimized to reduce mean squared error (MSE) or to increase concordance correlation. The objective evaluations and subjective evaluations of the results showed better performance for the condi- tional GAN model compared with these baseline systems. 

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5. Improving Speech Emotion Recognition Using Self-Supervised Learning with Domain-Specific Audiovisual Tasks

   https://doi.org/10.21437/Interspeech.2022-11012  

   Goncalves, Lucas ; Busso, Carlos ( September 2022 , Interspeech 2022)

   Speech emotion recognition (SER) is a challenging task due to the limited availability of real-world labeled datasets. Since it is easier to find unlabeled data, the use of self-supervised learning (SSL) has become an attractive alternative. This study proposes new pre-text tasks for SSL to improve SER. While our target application is SER, the proposed pre-text tasks include audio-visual formulations, leveraging the relationship between acoustic and facial features. Our proposed approach introduces three new unimodal and multimodal pre-text tasks that are carefully designed to learn better representations for predicting emotional cues from speech. Task 1 predicts energy variations (high or low) from a speech sequence. Task 2 uses speech features to predict facial activation (high or low) based on facial landmark movements. Task 3 performs a multi-class emotion recognition task on emotional labels obtained from combinations of action units (AUs) detected across a video sequence. We pre-train a network with 60.92 hours of unlabeled data, fine-tuning the model for the downstream SER task. The results on the CREMA-D dataset show that the model pre-trained on the proposed domain-specific pre-text tasks significantly improves the precision (up to 5.1%), recall (up to 4.5%), and F1-scores (up to 4.9%) of our SER system. 

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