arXiv:2408.13608v1 Announce Type: new
Abstract: 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.
Analyzing the Multi-disciplinary Nature of Speech-Language Multi-modal Learning
This article discusses the challenges in speech-language multi-modal learning and the need for a large-scale dataset that provides a comprehensive understanding of speech style. The author highlights the trade-off between data collection and high-quality annotation and proposes an automatic speech annotation system for expressiveness interpretation.
The multi-disciplinary nature of this topic is evident in the various techniques and technologies used in the proposed system. The speech audios are processed using expert classifiers and captioning models, which require expertise in speech recognition, natural language processing, and machine learning. The fine-tuned LLaMA (Language Learning and Modeling of Annotation) algorithm further enhances the system’s ability to generate customized annotations.
From the perspective of multimedia information systems, the article emphasizes the importance of combining audio and natural language data to gain insights into speech style. This integration of multiple modalities (speech and text) is crucial for developing sophisticated speech synthesis and speech style understanding systems.
The concept of animations is related to this topic as it involves the creation of expressive and vivid movements and gestures to convey meaning. In speech-language multi-modal learning, the annotations generated by the system aim to capture the expressive nuances of speech, similar to the way animations convey emotions and gestures.
Artificial reality (AR), augmented reality (AR), and virtual realities (VR) can also benefit from the advancements in speech-language multi-modal learning. These immersive technologies often incorporate speech interactions, and understanding speech style can enhance the realism and effectiveness of these experiences. For example, in AR and VR applications, realistic and expressive speech can contribute to more engaging and lifelike virtual experiences.
What’s Next?
The development of the automatic speech annotation system described in this article opens up new possibilities for future research and applications. Here are a few directions that could be explored:
- Improving Annotation Quality: While the proposed system provides tailored natural language descriptions, further research could focus on enhancing the accuracy and richness of the annotations. Advanced machine learning models and linguistic analysis techniques could be employed to generate even more nuanced descriptions of speech styles.
- Expanding the Dataset: Although the SpeechCraft dataset mentioned in the article is extensive, future work could involve expanding the dataset to include more languages, dialects, and speech styles. This would provide a broader understanding of speech variation and enable the development of more inclusive and diverse speech-synthesis and style-understanding models.
- Real-Time Annotation: Currently, the annotation system processes pre-recorded speech clips. An interesting direction for further research would be to develop real-time annotation systems that can interpret and annotate expressive speech in live conversations or presentations. This would have applications in communication technologies, public speaking training, and speech therapy.
- Integration with Virtual Reality: As mentioned earlier, integrating speech-style understanding into virtual reality experiences can enhance immersion and realism. Future work could focus on developing techniques to seamlessly integrate the proposed annotation system and the generated datasets with virtual reality environments, creating more interactive and immersive speech-driven virtual experiences.
Overall, the advancements in speech-language multi-modal learning discussed in this article have significant implications in various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The proposed automatic speech annotation system and the SpeechCraft dataset pave the way for further research and applications in speech synthesis, style understanding, and immersive technologies.