Search Results for author: Hu Hu

Found 15 papers, 7 papers with code

TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval

no code implementations2 Aug 2023 Kaibin Tian, Ruixiang Zhao, Hu Hu, Runquan Xie, Fengzong Lian, Zhanhui Kang, Xirong Li

For efficient T2VR, we propose TeachCLIP with multi-grained teaching to let a CLIP4Clip based student network learn from more advanced yet computationally heavy models such as X-CLIP, TS2-Net and X-Pool .

Retrieval text similarity +2

A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer

1 code implementation16 Oct 2021 Hu Hu, Sabato Marco Siniscalchi, Chao-Han Huck Yang, Chin-Hui Lee

We propose a variational Bayesian (VB) approach to learning distributions of latent variables in deep neural network (DNN) models for cross-domain knowledge transfer, to address acoustic mismatches between training and testing conditions.

Acoustic Scene Classification Scene Classification +1

Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Classification

1 code implementation8 Oct 2021 Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang, Hu Hu, Sabato Marco Siniscalchi, Pin-Yu Chen, Yu Tsao

In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system.

Spoken Command Recognition Transfer Learning

A Two-Stage Approach to Device-Robust Acoustic Scene Classification

1 code implementation3 Nov 2020 Hu Hu, Chao-Han Huck Yang, Xianjun Xia, Xue Bai, Xin Tang, Yajian Wang, Shutong Niu, Li Chai, Juanjuan Li, Hongning Zhu, Feng Bao, Yuanjun Zhao, Sabato Marco Siniscalchi, Yannan Wang, Jun Du, Chin-Hui Lee

To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.

Acoustic Scene Classification Classification +4

Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification

no code implementations31 Jul 2020 Hu Hu, Sabato Marco Siniscalchi, Yannan Wang, Chin-Hui Lee

In this paper, we propose a domain adaptation framework to address the device mismatch issue in acoustic scene classification leveraging upon neural label embedding (NLE) and relational teacher student learning (RTSL).

Acoustic Scene Classification Classification +3

An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances

no code implementations31 Jul 2020 Hu Hu, Sabato Marco Siniscalchi, Yannan Wang, Xue Bai, Jun Du, Chin-Hui Lee

In contrast to building scene models with whole utterances, the ASM-removed sub-utterances, i. e., acoustic utterances without stop acoustic segments, are then used as inputs to the AlexNet-L back-end for final classification.

Acoustic Scene Classification Classification +5

Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement

2 code implementations25 Jul 2020 Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee

Finally, our experiments of multi-channel speech enhancement on a simulated noisy WSJ0 corpus demonstrate that our proposed hybrid CNN-TT architecture achieves better results than both DNN and CNN models in terms of better-enhanced speech qualities and smaller parameter sizes.

regression Speech Enhancement

Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition

no code implementations1 May 2020 Hu Hu, Rui Zhao, Jinyu Li, Liang Lu, Yifan Gong

Recently, the recurrent neural network transducer (RNN-T) architecture has become an emerging trend in end-to-end automatic speech recognition research due to its advantages of being capable for online streaming speech recognition.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

L-Vector: Neural Label Embedding for Domain Adaptation

no code implementations25 Apr 2020 Zhong Meng, Hu Hu, Jinyu Li, Changliang Liu, Yan Huang, Yifan Gong, Chin-Hui Lee

We propose a novel neural label embedding (NLE) scheme for the domain adaptation of a deep neural network (DNN) acoustic model with unpaired data samples from source and target domains.

Domain Adaptation

Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network

2 code implementations3 Feb 2020 Jun Qi, Hu Hu, Yannan Wang, Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee

Finally, in 8-channel conditions, a PESQ of 3. 12 is achieved using 20 million parameters for TTN, whereas a DNN with 68 million parameters can only attain a PESQ of 3. 06.

regression Speech Enhancement

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