arXiv:2412.08988v1 Announce Type: cross Abstract: Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing methods have two primary deficiencies: (1) They struggle to simultaneously hold audio-visual sync and achieve clear pronunciation; (2) They lack the capacity to express user-defined emotions. To address these problems, we propose EmoDubber, an emotion-controllable dubbing architecture that allows users to specify emotion type and emotional intensity while satisfying high-quality lip sync and pronunciation. Specifically, we first design Lip-related Prosody Aligning (LPA), which focuses on learning the inherent consistency between lip motion and prosody variation by duration level contrastive learning to incorporate reasonable alignment. Then, we design Pronunciation Enhancing (PE) strategy to fuse the video-level phoneme sequences by efficient conformer to improve speech intelligibility. Next, the speaker identity adapting module aims to decode acoustics prior and inject the speaker style embedding. After that, the proposed Flow-based User Emotion Controlling (FUEC) is used to synthesize waveform by flow matching prediction network conditioned on acoustics prior. In this process, the FUEC determines the gradient direction and guidance scale based on the user’s emotion instructions by the positive and negative guidance mechanism, which focuses on amplifying the desired emotion while suppressing others. Extensive experimental results on three benchmark datasets demonstrate favorable performance compared to several state-of-the-art methods.
The article “EmoDubber: An Emotion-Controllable Dubbing Architecture” addresses the limitations of existing methods in movie dubbing tasks. These methods struggle to maintain audio-visual sync while achieving clear pronunciation and lack the ability to express user-defined emotions. To tackle these challenges, the authors propose EmoDubber, a dubbing architecture that allows users to specify emotion type and intensity while ensuring high-quality lip sync and pronunciation. The architecture includes Lip-related Prosody Aligning (LPA) to learn the consistency between lip motion and prosody variation, Pronunciation Enhancing (PE) to improve speech intelligibility, and a speaker identity adapting module to inject the desired speaker style. Additionally, the proposed Flow-based User Emotion Controlling (FUEC) synthesizes waveform by matching the user’s emotion instructions, amplifying the desired emotion while suppressing others. Experimental results on benchmark datasets demonstrate the superior performance of EmoDubber compared to state-of-the-art methods.
EmoDubber: Innovative Solutions for Movie Dubbing
The art of movie dubbing has come a long way in recent years, but there are still inherent challenges that need to be addressed. Existing methods often struggle to maintain audio-visual synchronization and clear pronunciation, while also lacking the ability to express user-defined emotions. In this article, we are excited to introduce EmoDubber, an emotion-controllable dubbing architecture that aims to revolutionize the dubbing industry.
Lip-related Prosody Aligning (LPA)
One of the key components of EmoDubber is the Lip-related Prosody Aligning (LPA). LPA focuses on learning the consistent relationship between lip motions and prosody variations by utilizing duration level contrastive learning. By incorporating reasonable alignment, EmoDubber ensures high-quality lip sync while maintaining natural prosody in the dubbed speech. This innovative approach tackles the long-standing challenge of achieving audio-visual synchronization without sacrificing pronunciation clarity.
Pronunciation Enhancing (PE)
To further enhance pronunciation intelligibility, EmoDubber utilizes a Pronunciation Enhancing (PE) strategy. This strategy involves fusing video-level phoneme sequences using an efficient conformer. By leveraging advanced technology, EmoDubber improves the clarity of speech, making it easier for viewers to understand the dubbed dialogue. With PE, EmoDubber sets a new standard for speech intelligibility in movie dubbing.
Speaker Identity Adapting
EmoDubber goes beyond simple lip sync and pronunciation improvement. It introduces the concept of speaker identity adapting, where acoustics prior is decoded and speaker style embedding is injected. This unique approach allows EmoDubber to capture the essence of the desired voice and replicate it accurately in the dubbed speech. By preserving the speaker’s identity, EmoDubber creates a more immersive and authentic dubbing experience.
Flow-based User Emotion Controlling (FUEC)
A groundbreaking feature of EmoDubber is the Flow-based User Emotion Controlling (FUEC) mechanism. FUEC enables users to specify the desired emotion type and intensity for the dubbed speech. Using a flow matching prediction network conditioned on acoustics prior, EmoDubber synthesizes waveforms that align with the specified emotion instructions. The positive and negative guidance mechanism allows users to amplify the desired emotion while suppressing others, resulting in a highly personalized and emotionally rich dubbing experience.
EmoDubber has been extensively tested on three benchmark datasets, and the results have been nothing short of impressive. Compared to several state-of-the-art methods, EmoDubber showcases superior performance in terms of audio-visual sync, pronunciation clarity, emotion expression, and overall user satisfaction. It represents a significant leap forward in the field of movie dubbing, opening up new possibilities for content creators and viewers alike.
As the demand for high-quality dubbed content continues to grow, EmoDubber sets a new standard of excellence in the industry. Its innovative solutions address the long-standing deficiencies in existing methods, providing clear pronunciation, user-defined emotions, and high-quality lip sync. EmoDubber is poised to redefine the dubbing landscape and pave the way for a more immersive and emotionally captivating viewing experience.
“EmoDubber: Shaping the Future of Movie Dubbing”
The paper titled “EmoDubber: An Emotion-Controllable Movie Dubbing Architecture” addresses two key challenges in movie dubbing: maintaining audio-visual synchronization and clear pronunciation, as well as expressing user-defined emotions. The existing methods in this field struggle to achieve both of these objectives simultaneously. However, the proposed EmoDubber architecture aims to overcome these limitations.
The authors introduce several novel techniques to improve the dubbing process. Firstly, they propose the Lip-related Prosody Aligning (LPA) method, which focuses on learning the inherent consistency between lip motion and prosody variation. By incorporating duration level contrastive learning, LPA ensures reasonable alignment between lip movements and speech prosody. This approach is crucial for achieving accurate lip sync in the dubbed videos.
To enhance speech intelligibility, the Pronunciation Enhancing (PE) strategy is introduced. PE utilizes an efficient conformer to fuse video-level phoneme sequences, improving the clarity of the generated speech. This technique addresses the pronunciation issues faced by existing methods, ensuring that the dubbing is not only synchronized but also easily understandable.
The paper also introduces a speaker identity adapting module, which aims to decode acoustics prior and inject the speaker style embedding. This technique helps in maintaining the desired voice characteristics in the generated speech, allowing for voice cloning.
One of the most significant contributions of this work is the proposed Flow-based User Emotion Controlling (FUEC) technique. FUEC enables users to specify the desired emotion type and intensity for the dubbed speech. By conditioning the synthesis waveform on acoustics prior, FUEC synthesizes speech that aligns with the video while expressing the desired emotion. The positive and negative guidance mechanisms of FUEC ensure that the desired emotion is amplified while suppressing other emotions. This capability to control emotions in the dubbed speech is a significant advancement in the field of movie dubbing.
The authors validate the effectiveness of the EmoDubber architecture by conducting extensive experiments on three benchmark datasets. The results demonstrate favorable performance compared to several state-of-the-art methods. This indicates that EmoDubber has the potential to significantly improve the quality of movie dubbing by addressing the challenges of audio-visual sync, clear pronunciation, and user-defined emotions.
In conclusion, the EmoDubber architecture proposed in this paper presents a comprehensive solution to the movie dubbing task. By incorporating techniques such as Lip-related Prosody Aligning, Pronunciation Enhancing, speaker identity adapting, and Flow-based User Emotion Controlling, the authors have overcome the deficiencies of existing methods. The experimental results indicate that EmoDubber outperforms state-of-the-art approaches and opens up new possibilities for high-quality, emotion-controllable movie dubbing.
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