Papers by Daijiao Liu

2 papers
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection (2024.emnlp-main)

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Challenge: Large language models (LLMs) are used for depression detection but their application remains unexplored.
Approach: They propose to integrate acoustic speech information into LLMs for depression detection by integrating aural landmarks into the framework.
Outcome: The proposed method adds critical dimensions to speech transcripts and provides insights into the unique speech patterns of individuals.
Speaking in Wavelet Domain: A Simple and Efficient Approach to Speed up Speech Diffusion Model (2024.emnlp-main)

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Challenge: Existing approaches to enhance inference speed and training require complex modifications to the model.
Approach: They propose to double the training and inference speed of Denoising Diffusion Probabilistic Models by simply redirecting the generative target to the wavelet domain.
Outcome: The proposed method doubles the training and inference speed of Speech DDPMs by redirecting the generative target to the wavelet domain.

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