Generating Accurate and Rhythmic Cued Speech Gestures with Gloss-Prompted Diffusion Model

Core Concepts

A novel Gloss-Prompted Diffusion-based framework (GlossDiff) that can simultaneously generate fine-grained hand position, finger movements, and lip movements for Cued Speech, while also capturing the natural rhythm dynamics of the gestures.

Abstract

The paper proposes a novel Gloss-Prompted Diffusion-based Cued Speech (CS) Gesture Generation framework called GlossDiff. The key highlights are:

Gloss Knowledge Infusion Module: The authors introduce a "gloss" - a descriptive text instruction that directly links the spoken language to the corresponding CS gestures. This gloss helps bridge the gap between text/audio and the fine-grained CS hand/finger movements.

Audio-driven Rhythmic Module (ARM): The authors suggest that rhythm is an important paralinguistic feature for CS, and propose a module to learn the rhythm dynamics that match the input audio speech. This helps generate more natural and synchronized CS gestures.

Diffusion-based Generation: The authors design a Gloss-Prompted Diffusion Model that can generate accurate hand and finger movements for CS, leveraging the gloss prompts.

New Chinese CS Dataset: The authors create and publish the first large-scale Mandarin Chinese CS dataset with 4 cuers and 4000 sentences, to enable research in this domain.

Extensive experiments on the new dataset show the proposed GlossDiff framework outperforms state-of-the-art methods in both quantitative and qualitative evaluations, demonstrating its effectiveness in generating fine-grained and rhythmic CS gestures.

Stats

More than 5% of the global population (466 million) suffers from hearing loss.
Cued Speech (CS) can effectively promote non-barrier communication for the hearing-impaired.
The new Mandarin Chinese CS (MCCS) dataset contains 4000 CS videos from 4 cuers.

Quotes

"Cued Speech (CS) is an advanced visual phonetic encoding system that integrates lip reading with hand codings, enabling people with hearing impairments to communicate efficiently."
"Rhythm is a critical paralinguistic information in spoken language. As a coding system for spoken languages, we suggest natural rhythm dynamics should also be considered as a very important feature for CS's complete semantic expression."

Key Insights Distilled From

Bridge to Non-Barrier Communication: Gloss-Prompted Fine-grained Cued Speech Gesture Generation with Diffusion Model

by Wentao Lei,L... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19277.pdf

Deeper Inquiries

How can the proposed GlossDiff framework be extended to generate CS gestures for other languages beyond Mandarin Chinese?

The GlossDiff framework can be extended to generate CS gestures for other languages by adapting the gloss generation process to the specific phonetic encoding rules of each language. Since CS is a coding system of spoken language, the key lies in understanding the unique hand codings and lip movements required for each phoneme in the target language. By incorporating linguistic rules knowledge specific to the language, a gloss knowledge infusion module can be designed to generate descriptive text that establishes a direct semantic connection between the spoken language and CS gestures. This module can be trained on a new dataset for the target language, capturing the nuances of hand positions, finger shapes, and lip movements required for accurate CS gesture generation. Additionally, the diffusion-based generation module can be fine-tuned on the new dataset to ensure the generated CS gestures align with the phonetic characteristics of the target language.

What are the potential applications of the generated fine-grained and rhythmic CS gestures beyond communication, such as in the field of human-computer interaction or virtual reality?

The generated fine-grained and rhythmic CS gestures have a wide range of potential applications beyond communication. In the field of human-computer interaction, these gestures can be used to enhance user interfaces by enabling more intuitive and natural interactions with devices. For example, CS gestures can be integrated into gesture recognition systems to control computers, smartphones, or other digital devices through hand and finger movements. This can improve accessibility for individuals with hearing impairments and provide an alternative input method for users in various contexts.
In virtual reality (VR), the generated CS gestures can be utilized to create more realistic and immersive experiences. By incorporating accurate hand positions, finger shapes, and lip movements into virtual avatars or characters, users can communicate and interact with virtual environments in a more natural and expressive manner. This can enhance the sense of presence and engagement in VR applications, such as virtual meetings, training simulations, or gaming experiences. The rhythmic quality of the gestures can also add a layer of realism and synchronization with audio cues, further enhancing the overall user experience.

Can the gloss generation process be further automated and optimized to reduce the manual effort required in the current framework?

Yes, the gloss generation process can be further automated and optimized to reduce manual effort in the GlossDiff framework. One approach to automate the gloss generation is to explore the use of natural language processing (NLP) techniques, such as transformer-based models like GPT (Generative Pre-trained Transformer), to generate descriptive text based on the input audio or text. These models can be trained on a large corpus of CS data to learn the mapping between spoken language and CS gestures, enabling them to generate gloss instructions more efficiently and accurately.
Additionally, the integration of real-time speech recognition technology can streamline the gloss generation process by automatically transcribing spoken language into text inputs for the framework. This can eliminate the need for manual input of text and reduce the overall processing time. By leveraging advancements in machine learning and speech processing algorithms, the gloss generation module can be optimized to handle a variety of languages and dialects, making the framework more versatile and user-friendly.