Company Filing History:
Years Active: 2022-2023
Title: Innovations of Zhengkun Tian
Introduction
Zhengkun Tian is a prominent inventor based in Beijing, China. He has made significant contributions to the field of audio detection and speech recognition. With a total of three patents to his name, his work showcases innovative approaches to technology.
Latest Patents
Zhengkun Tian's latest patents include a "Hierarchical Generated Audio Detection System." This system comprises an audio preprocessing module, a CQCC feature extraction module, a LFCC feature extraction module, a first-stage lightweight coarse-level detection model, and a second-stage fine-level deep identification model. The audio preprocessing module processes collected audio or video data to obtain an audio clip within a specified length. The CQCC and LFCC feature extraction modules then extract relevant features for further analysis. The system effectively screens and identifies real and generated audio through a two-stage detection process.
Another notable patent is the "Method for Training Speech Recognition Model." This invention relates to the field of speech recognition and involves inputting audio training samples into an acoustic encoder to represent their features in an encoded manner. The process includes determining a text prediction vector and calculating loss functions for iterative optimization, enhancing the accuracy of speech recognition systems.
Career Highlights
Zhengkun Tian is affiliated with the Chinese Academy of Sciences, where he continues to advance research in audio detection and speech recognition technologies. His work has garnered attention for its innovative methodologies and practical applications.
Collaborations
Zhengkun Tian collaborates with esteemed colleagues such as Jianhua Tao and Jiangyan Yi. Their combined expertise contributes to the advancement of research in their respective fields.
Conclusion
Zhengkun Tian's contributions to audio detection and speech recognition exemplify the spirit of innovation. His patents reflect a commitment to enhancing technology and improving user experiences in audio processing.
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