Generating Synthetic Data to Improve Joint Multimodal Speech-and-Gesture Synthesis

Core Concepts

Using synthetic data generated by unimodal speech and gesture synthesis models to improve the quality and controllability of joint multimodal speech-and-gesture synthesis.

Abstract

The paper proposes a method to address the data shortage in joint multimodal speech-and-gesture synthesis by generating synthetic training data. The key steps are:

Generating conversational text using a large language model (GPT-4) with prompting to capture spontaneous speech patterns.
Synthesizing diverse speech audio from the generated text using a non-autoregressive text-to-speech model (XTTS).
Filtering and aligning the synthetic speech data using automatic speech recognition (Whisper) and forced alignment (Montreal Forced Aligner).
Generating co-speech gestures from the synthetic speech audio and aligned text using a state-of-the-art diffusion-based gesture synthesis model.

The authors then extend the existing state-of-the-art joint speech-and-gesture synthesis model (Match-TTSG) in two ways:

Incorporating a probabilistic duration model and individual models for pitch and energy prediction to enable better prosody modeling and control.
Adding a speaker embedding to enable multispeaker synthesis.

The proposed system, called MAGI, is evaluated through subjective user studies on speech quality, gesture quality, and the appropriateness of the generated speech and motion. The results show that pre-training on the synthetic data significantly improves the quality of both the speech and gestures compared to training only on the limited real data. The architectural improvements in MAGI further enhance the synthesis quality and controllability when pre-trained on the synthetic data.

Stats

The synthetic training dataset contains 37.6 hours of multimodal data across 8,173 utterances.
The real target dataset (TSGD2) contains 4.5 hours of training data.
The Word Error Rate (WER) of the synthesized speech was reduced from 13.28% to 9.29% by fine-tuning on the synthetic data.

Quotes

"Pre-training on synthetic data markedly enhanced the quality of synthesised speech, though adjustments to the architecture did not significantly alter its naturalness."
"Notably, MAGI facilitated greater control over pitch and energy – a feature absent from Match-TTSG."

Key Insights Distilled From

Fake it to make it: Using synthetic data to remedy the data shortage in joint multimodal speech-and-gesture synthesis

by Shiv... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19622.pdf

Deeper Inquiries

How could the synthetic data generation pipeline be further improved to better capture the cross-modal dependencies between speech and gesture?

To enhance the synthetic data generation pipeline for capturing cross-modal dependencies between speech and gesture more effectively, several improvements can be considered:

Improved Alignment Techniques: Implement more advanced alignment techniques to ensure precise synchronization between speech audio and gesture motion. This can involve refining the forced alignment process to achieve more accurate word-level timestamps for the text transcriptions.

Incorporation of Contextual Information: Integrate contextual information from the speech content to guide the generation of corresponding gestures. This can involve leveraging semantic analysis of the speech text to inform the generation of gestures that align closely with the intended meaning.

Multi-Modal Representation Learning: Explore techniques for learning joint representations of speech and gesture data to capture the inherent correlations between the two modalities. This can involve training models that can extract shared features from both speech and gesture inputs.

Data Augmentation Strategies: Implement data augmentation strategies to introduce variability in the synthetic data, mimicking the natural diversity found in human speech and gesture interactions. This can help the models generalize better to unseen data and improve the quality of the generated outputs.

How could the proposed approach be extended to other multimodal synthesis tasks, such as generating facial expressions or body language alongside speech?

The proposed approach can be extended to other multimodal synthesis tasks, such as generating facial expressions or body language alongside speech, by considering the following strategies:

Multi-Modal Fusion: Develop models that can effectively fuse information from multiple modalities, such as speech, gesture, facial expressions, and body language. This can involve designing architectures that can handle the complexity of multiple input sources and generate coherent outputs.

Unified Representation Learning: Explore methods for learning unified representations that capture the relationships between different modalities. By training models to understand the correlations between speech, gestures, facial expressions, and body language, more contextually relevant and synchronized outputs can be generated.

Transfer Learning: Utilize transfer learning techniques to adapt the existing framework to new multimodal synthesis tasks. By leveraging pre-trained models on related tasks, the system can learn to generate diverse and expressive outputs across different modalities.

Fine-Grained Control Mechanisms: Implement fine-grained control mechanisms to enable users to manipulate the generated outputs in real-time. This can involve interactive interfaces that allow users to adjust parameters related to speech, gestures, facial expressions, and body language to achieve desired communication outcomes.

What other architectural modifications or training techniques could be explored to improve the appropriateness of the generated speech-gesture combinations?

To enhance the appropriateness of the generated speech-gesture combinations, the following architectural modifications and training techniques could be explored:

Attention Mechanisms: Incorporate attention mechanisms in the architecture to enable the model to focus on relevant parts of the input data during synthesis. This can help improve the alignment between speech content and corresponding gestures, leading to more contextually appropriate outputs.

Adversarial Training: Explore adversarial training techniques to encourage the model to generate more realistic and coherent speech-gesture combinations. By training the model to distinguish between real and generated outputs, it can learn to produce outputs that closely resemble natural human communication.

Dynamic Context Modeling: Implement dynamic context modeling techniques to capture the temporal dependencies between speech and gestures. By considering the sequential nature of communication, the model can generate more temporally coherent and contextually appropriate outputs.

Multi-Task Learning: Explore multi-task learning frameworks where the model is trained on multiple related tasks simultaneously, such as speech synthesis, gesture generation, and facial expression modeling. This can help the model learn shared representations and improve the overall appropriateness of the generated multimodal outputs.