TLDR: This research paper introduces an end-to-end pipeline for generating character-specific and emotion-aware speech from digital comics. The system integrates image processing for character and emotion detection, a Large Language Model (LLM) for dialogue attribution and emotion analysis using visual and plot context, and a Text-to-Speech (TTS) model for synthesizing expressive voices. It addresses challenges in character identification and emotion classification, demonstrating a significant step towards creating immersive and interactive comic reading experiences through automated voiceovers.
Digital comics have seen a surge in popularity, opening new avenues for engaging readers through advanced media processing. One particularly exciting development is the automatic generation of audiobooks from comics, transforming static dialogue into dynamic and expressive speech. This innovation promises to make digital reading experiences more immersive and accessible for everyone.
Traditionally, creating speech for comics involves several complex steps: analyzing images to detect characters and text, identifying characters within panels, attributing dialogue to the correct speaker, recognizing the emotional tone of the dialogue, and finally, synthesizing speech with appropriate emotional control. While each of these components has been studied individually, integrating them into a seamless, end-to-end system has remained a significant challenge.
A new research paper, “Emotion-Aware Speech Generation with Character-Specific Voices for Comics”, introduces a comprehensive, end-to-end pipeline designed to generate character-specific, emotion-aware speech directly from full comic volumes. This system takes the entire comic as input and produces speech that is perfectly aligned with each character’s dialogue and emotional state.
How the System Works
The proposed pipeline integrates several advanced modules:
- Image Processing: This module is responsible for detecting characters, recognizing text, and estimating emotion intensity from the comic panels.
- Large Language Model (LLM): An LLM plays a crucial role in attributing dialogue to the correct characters and analyzing emotions. It achieves this by combining visual information with the evolving plot context, allowing it to understand the narrative nuances.
- Text-to-Speech (TTS) Synthesis: Finally, speech is synthesized using a sophisticated TTS model. This model is capable of generating distinct voice profiles tailored to each character and can also control the emotional expression of the speech.
The researchers built upon existing comic datasets like Manga109 and KangaiSet, which provide valuable annotations for dialogue, speaker IDs, and character facial emotions. However, they also tackled several underexplored challenges, such as the difficulty of reliable visual character identification due to varied appearances, the complexity of speaker attribution when dialogue is far from the character, and the issue of imbalanced datasets for emotion classification.
To overcome these hurdles, the system combines vision-based approaches, like CNNs for character and emotion recognition, with the powerful contextual reasoning capabilities of LLMs. This hybrid approach allows for robust speaker and emotion inference by integrating both visual cues and textual information.
Key Contributions
The paper highlights several important contributions:
- The design of a complete pipeline for comic speech generation.
- A novel emotion prediction method that simplifies initial image-based classification, followed by fine-grained classification using an LLM.
- A systematic investigation into how LLMs can effectively use both visual and textual information to predict speakers and emotions from comic dialogues.
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Performance and Insights
Experiments demonstrated that the system achieves moderate accuracy in character recognition and emotion classification, even with limited annotated data. The character identification module performed reliably across various comic styles, though errors at this stage could impact downstream tasks like speaker attribution.
The LLM-based dialogue and emotion inference showed promise but also revealed sensitivities to the complexity of visual input and ambiguities in emotion labels, particularly for neutral expressions. For instance, the LLM sometimes labeled interrogative sentences as ‘surprise,’ even if the ground truth was ‘neutral.’ However, from a voice generation perspective, such misclassifications might still produce perceptually appropriate expressions.
The ultimate goal is to generate speech that correctly identifies both the speaker and the appropriate emotional tone for each dialogue segment. The system achieved a joint accuracy of 20.4% for this end-to-end task. While this figure might seem modest, it reflects the combined difficulty of two challenging tasks, compounded by upstream errors and inherent ambiguities in comic expressions. The researchers note that human inspection often reveals that some ‘errors’ can still result in perceptually appropriate outputs, highlighting a gap between current evaluation metrics and human judgment.
In conclusion, this work demonstrates the feasibility of automatic, expressive comic voiceovers and lays a strong foundation for developing more robust and human-aligned audiovisual storytelling systems in the future.
