Automatic video commentary systems are widely used on multimedia social media platforms to extract factual information about video content. However, current systems may overlook essential paralinguistic cues, including emotion and attitude, which are critical for fully conveying the meaning of visual content. The absence of these cues can limit user understanding or, in some cases, distort the video’s original intent. Expressive speech effectively conveys these cues and enhances the user’s comprehension of videos. Building on these insights, this paper explores the usage of vision-context-aware expressive speech in enhancing users’ understanding of videos in video commentary systems1. Firstly, our formatting study indicates that semantic-only speech can lead to ambiguity, and misaligned emotions between speech and visuals may distort content interpretation. To address this, we propose a method called vision-context-aware speech synthesis (V-CASS). It analyzes para-linguistic cues from visuals using a vision-language model and leverages a knowledge-infused language model to guide the expressive speech model in generating context-aligned speech. User studies show that V-CASS enhances emotional and attitudinal resonance, as well as user audio-visual understanding and engagement, with 74.68% of participants preferring the system. Finally, we explore the potential of our method in helping blind and low-vision users navigate web videos, improving universal accessibility.

Imagine James Bond speaking like Mr. Bean---such a mismatch would create a jarring dissonance and break the viewer's immersion. Current research on virtual avatar animation has focused on modeling 3D geometry, appearance, motion generation, however, neglecting the harmony between speech prosody and the avatar's visual presentation and contextual environment. In this paper, we seek to bridge this gap by firstly identifying and defining the key elements necessary for achieving audiovisual harmony, such as appearance, expression, body posture, backgrounds and colors. Subsequently, we propose a method that jointly models semantic consistency in avatar animation, named HarmoniVox, specifically on crafting prosodic speech consistent with the avatar's essence from given visual image. To achieve this, we implement a technical framework with a mutual modal contrastive learning strategy, enhancing multimodal alignment in a coarse-to-fine fashion. To support this method, we establish a experimental dataset HarAvaSpeech comprising 28,929 image-audio pairs, designed to encompass expressive speech prosody and rich avatar visual presentations across a wide range of contexts. Leveraging this dataset, our experiments demonstrate that the proposed method outperforms the baselines in manipulating the nuanced tone and harmonious rhythm of speech with the avatar visual presentations, and reveal generalizability on out-of-domain cases. Demo would be provided in https://harmonivox.github.io/harmonivox/.

Speech-language multi-modal learning presents a significant challenge due to the fine nuanced information inherent in speech styles. Therefore, a large-scale dataset providing elaborate comprehension of speech style is urgently needed to facilitate insightful interplay between speech audio and natural language. However, constructing such datasets presents a major trade-off between large-scale data collection and high-quality annotation. To tackle this challenge, we propose an automatic speech annotation system for expressiveness interpretation that annotates in-the-wild speech clips with expressive and vivid human language descriptions. Initially, speech audios are processed by a series of expert classifiers and captioning models to capture diverse speech characteristics, followed by a fine-tuned LLaMA for customized annotation generation. Unlike previous tag/templet-based annotation frameworks with limited information and diversity, our system provides in-depth understandings of speech style through tailored natural language descriptions, thereby enabling accurate and voluminous data generation for large model training. With this system, we create SpeechCraft, a fine-grained bilingual expressive speech dataset. It is distinguished by highly descriptive natural language style prompts, containing approximately 2,000 hours of audio data and encompassing over two million speech clips. Extensive experiments demonstrate that the proposed dataset significantly boosts speech-language task performance in stylist speech synthesis and speech style understanding.

Lyrics and music are both significant for a singer to perform a song. Therefore, it is important in singer's motion generation to model both semantic and acoustic correlation with motions at the same time. In this paper, we propose HoloSinger, a novel comprehensive system that synthesizes singing motions according to the given song. Additionally, we present singing avatar with octahedral holographic projection. For singing motion generation, we introduce a Transformer-VAE generative model to decompose lyrics and music, then fuse their impacts to synthesize singer's motions. Extensive experiments and user studies show that our method automatically generates realistic motions that adhere to musical choreography and reflect the lyric semantics appropriately. Furthermore, we design a desktop-level holographic projection device with an octahedral structure. It achieves high-definition holographic projection effects with smaller volume, larger imaging area ratio, and the ability of real-time AI interaction.