Search Results for author: Taihao Li

Found 6 papers, 2 papers with code

Disentangling Prosody Representations with Unsupervised Speech Reconstruction

no code implementations14 Dec 2022 Leyuan Qu, Taihao Li, Cornelius Weber, Theresa Pekarek-Rosin, Fuji Ren, Stefan Wermter

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information.

Association Automatic Speech Recognition +6

Parameter-Efficient Tuning on Layer Normalization for Pre-trained Language Models

no code implementations16 Nov 2022 Wang Qi, Yu-Ping Ruan, Yuan Zuo, Taihao Li

Conventional fine-tuning encounters increasing difficulties given the size of current Pre-trained Language Models, which makes parameter-efficient tuning become the focal point of frontier research.

Data Augmentation with Unsupervised Speaking Style Transfer for Speech Emotion Recognition

no code implementations16 Nov 2022 Leyuan Qu, Wei Wang, Taihao Li, Cornelius Weber, Stefan Wermter, Fuji Ren

Once training is completed, EmoAug enriches expressions of emotional speech in different prosodic attributes, such as stress, rhythm and intensity, by feeding different styles into the paralinguistic encoder.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Twin Contrastive Learning for Online Clustering

2 code implementations21 Oct 2022 Yunfan Li, Mouxing Yang, Dezhong Peng, Taihao Li, Jiantao Huang, Xi Peng

Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.

Contrastive Learning Deep Clustering +2

Fast sensor placement by enlarging principle submatrix for large-scale linear inverse problems

no code implementations6 Oct 2021 Fen Wang, Gene Cheung, Taihao Li, Ying Du, Yu-Ping Ruan

Sensor placement for linear inverse problems is the selection of locations to assign sensors so that the entire physical signal can be well recovered from partial observations.

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