Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity (2009.02119v1)

Abstract: For human-like agents, including virtual avatars and social robots, making proper gestures while speaking is crucial in human--agent interaction. Co-speech gestures enhance interaction experiences and make the agents look alive. However, it is difficult to generate human-like gestures due to the lack of understanding of how people gesture. Data-driven approaches attempt to learn gesticulation skills from human demonstrations, but the ambiguous and individual nature of gestures hinders learning. In this paper, we present an automatic gesture generation model that uses the multimodal context of speech text, audio, and speaker identity to reliably generate gestures. By incorporating a multimodal context and an adversarial training scheme, the proposed model outputs gestures that are human-like and that match with speech content and rhythm. We also introduce a new quantitative evaluation metric for gesture generation models. Experiments with the introduced metric and subjective human evaluation showed that the proposed gesture generation model is better than existing end-to-end generation models. We further confirm that our model is able to work with synthesized audio in a scenario where contexts are constrained, and show that different gesture styles can be generated for the same speech by specifying different speaker identities in the style embedding space that is learned from videos of various speakers. All the code and data is available at https://github.com/ai4r/Gesture-Generation-from-Trimodal-Context.

Insights on "Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity"

The paper "Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity" presents a sophisticated model for generating human-like gestures synchronized with speech. The paper explores the intricate task of co-speech gesture generation, leveraging a blend of text, audio, and speaker identity, thus advancing beyond conventional mono-modal approaches that primarily focused on speech text or audio alone.

Technical Overview

The proposed model introduces a trimodal framework which simultaneously processes speech content, rhythm, and speaker-specific gestural style. This multimodal integration is architected through a temporally synchronized encoder-decoder setup, incorporating adversarial training for enhanced gesture realism. The gesture generator is adept at producing upper-body gestures that resonate with the audio’s rhythm and content, contrary to prior models which either overlooked speaker styles or restricted themselves to less expressive gesture representations.

Key innovations include:

1. Multimodal Encoders: The model employs distinct encoders for text, audio, and speaker identity. The text encoder utilizes a temporal convolutional network, while the audio encoder processes raw audio waveforms using successive convolutional layers. Speaker identity is encoded via a style embedding mechanism that highlights individualized gesture styles.
2. Adversarial Training: An adversarial scheme is employed to differentiate between human-like gestures and generated ones, utilizing binary classification to refine the realism of synthesized gestures.
3. Style Embedding Space: The paper pioneers a style embedding space for speaker identity, encouraging gesture diversity by embedding style elements that span the variability seen in human interactions.

Quantitative and Qualitative Evaluation

The researchers introduce a novel evaluation metric—Fréchet Gesture Distance (FGD)—emulating the Fréchet Inception Distance used in image generation domains. FGD aims to overcome the limitations of conventional metrics like Mean Absolute Error by considering the perceptual quality and diversity of gesture generation, validated using human evaluation through synthetic tests.

Quantitative results showed superior performance of the proposed approach over state-of-the-art models. Interestingly, different modalities contributed variably to model performance, with speaker identity playing a critical role in stylistic variability. In qualitative assessments, the generated gestures were well-aligned with speech content and tempo, demonstrating the model's capacity to synthesize expressive co-speech gestures.

Implications and Future Directions

This research imparts significant implications for the field of human-agent interaction, particularly in applications requiring virtual avatars and social robots to convey human-like nuances. By incorporating personalized gestural variations and effective speech synchronization, the model paves the way for more natural interactions in dynamic environments such as virtual reality, gaming, and customer service interfaces.

The paper acknowledges avenues for further exploration, including extending gesture control mechanisms and refining evaluation metrics for more sophisticated fidelity assessments. Additionally, integrating finer elements such as facial expressions and hand articulations alongside diversified environmental contexts remains an open challenge, presenting ample scope for ensuing research.

Conclusion

With its comprehensive approach to trimodal context processing, the paper offers significant advancements in gestural generation models, highlighting the benefits of incorporating multimodal signals for enhancing agent lifelikeness and interaction quality. The implications resonate deeply within artificial intelligence, particularly in advancing human-computer interfaces towards richer, more intuitive exchanges.