Search Results for author: Chin-Hui Lee

Found 29 papers, 6 papers with code

A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning

no code implementations17 Feb 2022 Hengshun Zhou, Jun Du, Chao-Han Huck Yang, Shifu Xiong, Chin-Hui Lee

Audio-only-based wake word spotting (WWS) is challenging under noisy conditions due to environmental interference in signal transmission.

Network Pruning

The USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party meeting transcription (M2MeT) challenge

no code implementations10 Feb 2022 Maokui He, Xiang Lv, Weilin Zhou, JingJing Yin, Xiaoqi Zhang, Yuxuan Wang, Shutong Niu, Yuhang Cao, Heng Lu, Jun Du, Chin-Hui Lee

We propose two improvements to target-speaker voice activity detection (TS-VAD), the core component in our proposed speaker diarization system that was submitted to the 2022 Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenge.

Action Detection Activity Detection +1

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

Separation Guided Speaker Diarization in Realistic Mismatched Conditions

no code implementations6 Jul 2021 Shu-Tong Niu, Jun Du, Lei Sun, Chin-Hui Lee

We propose a separation guided speaker diarization (SGSD) approach by fully utilizing a complementarity of speech separation and speaker clustering.

Speaker Diarization Speech Separation

PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification

no code implementations2 Apr 2021 Chao-Han Huck Yang, Sabato Marco Siniscalchi, Chin-Hui Lee

We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech applications.

Ranked #3 on Keyword Spotting on Google Speech Commands (10-keyword Speech Commands dataset metric)

Keyword Spotting

Lip-reading with Hierarchical Pyramidal Convolution and Self-Attention

no code implementations28 Dec 2020 Hang Chen, Jun Du, Yu Hu, Li-Rong Dai, Chin-Hui Lee, Bao-Cai Yin

In this paper, we propose a novel deep learning architecture to improving word-level lip-reading.

Lip Reading

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 +3

Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition

2 code implementations26 Oct 2020 Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee

Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.

 Ranked #1 on Keyword Spotting on Google Speech Commands (10-keyword Speech Commands dataset metric)

Automatic Speech Recognition Federated Learning +1

A Noise-Aware Memory-Attention Network Architecture for Regression-Based Speech Enhancement

no code implementations25 Oct 2020 Yu-Xuan Wang, Jun Du, Li Chai, Chin-Hui Lee, Jia Pan

We propose a novel noise-aware memory-attention network (NAMAN) for regression-based speech enhancement, aiming at improving quality of enhanced speech in unseen noise conditions.

Frame Speech Enhancement

Correlating Subword Articulation with Lip Shapes for Embedding Aware Audio-Visual Speech Enhancement

no code implementations21 Sep 2020 Hang Chen, Jun Du, Yu Hu, Li-Rong Dai, Bao-Cai Yin, Chin-Hui Lee

We first extract visual embedding from lip frames using a pre-trained phone or articulation place recognizer for visual-only EASE (VEASE).

Speech Enhancement

On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression

no code implementations12 Aug 2020 Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression.

Speech Enhancement

Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression

no code implementations4 Aug 2020 Jun Qi, Jun Du, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee

In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error.

Learning Theory Speech Enhancement

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 +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

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.

Speech Enhancement

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

Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement

no code implementations31 Mar 2020 Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Chin-Hui Lee

Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems.

Automatic Speech Recognition Data Augmentation +1

Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning

no code implementations20 Feb 2020 Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, I-Te Danny Hung, Chin-Hui Lee, Xiaoli Ma

Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e. g., autonomous navigation and continuous robot arm control.)

Autonomous Navigation online learning +1

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.

Speech Enhancement

Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks

no code implementations ICLR 2019 Jun Qi, Chin-Hui Lee, Javier Tejedor

The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs).

Machine Translation Translation

Convolutional-Recurrent Neural Networks for Speech Enhancement

no code implementations2 May 2018 Han Zhao, Shuayb Zarar, Ivan Tashev, Chin-Hui Lee

By incorporating prior knowledge of speech signals into the design of model structures, we build a model that is more data-efficient and achieves better generalization on both seen and unseen noise.

Speech Enhancement

Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement

no code implementations21 Mar 2017 Yong Xu, Jun Du, Zhen Huang, Li-Rong Dai, Chin-Hui Lee

We propose a multi-objective framework to learn both secondary targets not directly related to the intended task of speech enhancement (SE) and the primary target of the clean log-power spectra (LPS) features to be used directly for constructing the enhanced speech signals.

Sound

A Probabilistic Framework for Representing Dialog Systems and Entropy-Based Dialog Management through Dynamic Stochastic State Evolution

no code implementations27 Apr 2015 Ji Wu, Miao Li, Chin-Hui Lee

A Song-On-Demand task, with a total of 38117 songs and 12 attributes corresponding to each song, is used to test the performance of the proposed approach.

Automatic Speech Recognition Natural Language Understanding

Maximum a Posteriori Adaptation of Network Parameters in Deep Models

no code implementations6 Mar 2015 Zhen Huang, Sabato Marco Siniscalchi, I-Fan Chen, Jiadong Wu, Chin-Hui Lee

We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance.

Automatic Speech Recognition

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