47 citations · 123 across the 17 of their papers we have counts for
9 papers · 1 filter
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge Transfer
Hu Hu, Sabato Marco Siniscalchi, Chao-Han Huck Yang +1
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 ac…
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang +4
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to mo…
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Hu Hu, Xuesong Yang, Zeynab Raeesy +6
Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address this problem by building an accent-robust RNN-T system with domain adversarial training (DA…
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
Hu Hu, Chao-Han Huck Yang, Xianjun Xia +13
In this technical report, we present a joint effort of four groups, namely GT, USTC, Tencent, and UKE, to tackle Task 1 - Acoustic Scene Classification (ASC) in the DCASE 2020 Chal…
Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement
Jun Qi, Hu Hu, Yannan Wang +3
This paper investigates different trade-offs between the number of model parameters and enhanced speech qualities by employing several deep tensor-to-vector regression models for s…
Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification
Hu Hu, Sabato Marco Siniscalchi, Yannan Wang +1
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 relat…
An Acoustic Segment Model Based Segment Unit Selection Approach to Acoustic Scene Classification with Partial Utterances
Hu Hu, Sabato Marco Siniscalchi, Yannan Wang +3
In this paper, we propose a sub-utterance unit selection framework to remove acoustic segments in audio recordings that carry little information for acoustic scene classification (…
L-Vector: Neural Label Embedding for Domain Adaptation
Zhong Meng, Hu Hu, Jinyu Li +4
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 dom…
Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network
Jun Qi, Hu Hu, Yannan Wang +3
We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key…