skip to main content

Title: Speech-Driven Expressive Talking Lips with Conditional Sequential Generative Adversarial Networks
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.  more » « less
Award ID(s):
Author(s) / Creator(s):
Date Published:
Journal Name:
IEEE Transactions on Affective Computing
Page Range / eLocation ID:
1 to 1
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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. 
    more » « less
  2. Thefaceconveysablendofverbalandnonverbalinformation playing an important role in daily interaction. While speech articulation mostly affects the orofacial areas, emotional behaviors are externalized across the entire face. Considering the relation between verbal and non-verbal behaviors is important to create naturalistic facial movements for conversational agents (CAs). Furthermore, facial muscles connect areas across the face, creating principled relationships and dependencies between the movements that have to be taken into account. These relationships are ignored when facial movements across the face are sep- arately generated. This paper proposes to create speech-driven models that jointly capture the relationship not only between speech and facial movements, but also across facial movements. The input to the models are features extracted from speech that convey the verbal and emotional states of the speakers. We build our models with bidirectional long-short term memory (BLSTM) units which are shown to be very successful in modeling dependencies for sequential data. The objective and subjective evaluations of the results demonstrate the benefits of joint modeling of facial regions using this framework. 
    more » « less
  3. 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. 
    more » « less
  4. The ability to identify speech with similar emotional content is valuable to many applications, including speech retrieval, surveillance, and emotional speech synthesis. While current formulations in speech emotion recognition based on classification or regression are not appropriate for this task, solutions based on preference learning offer appealing approaches for this task. This paper aims to find speech samples that are emotionally similar to an anchor speech sample provided as a query. This novel formulation opens interesting research questions. How well can a machine complete this task? How does the accuracy of automatic algorithms compare to the performance of a human performing this task? This study addresses these questions by training a deep learning model using a triplet loss function, mapping the acoustic features into an embedding that is discriminative for this task. The network receives an anchor speech sample and two competing speech samples, and the task is to determine which of the candidate speech sample conveys the closest emotional content to the emotion conveyed by the anchor. By comparing the results from our model with human perceptual evaluations, this study demonstrates that the proposed approach has performance very close to human performance in retrieving samples with similar emotional content. 
    more » « less
  5. 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. 
    more » « less