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Task-Adaptive Generative Adversarial Network Based Speech Dereverberation for Robust Speech Recognition 1

引用

18th National Conference on Man-Machine Speech Communication, NCMMSC 2023

作者： Liu, Ji Li, Nan Ge, Meng Fu, Yanjie Wang, Longbiao Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Japan Advanced Institute of Science and Technology Ishikawa Nomi Japan

ISBN: (数字)9789819706013

ISBN: (纸本)9789819706006

Reverberation is known to severely affect speech recognition performance when speech is recorded in an enclosed space. Deep learning-based speech dereverberation has been remarkably successful in recent years, achieving superior recognition performance for far-field speech applications. However, the output from conventional dereverberation systems cannot be guaranteed suitable for back-end recognition systems because of their different task goals. To bridge the gap between the front-end dereverberation and the back-end recognition, we propose a novel task-adaptive speech dereverberation generative adversarial network (GAN) based speech dereverberation model called Task-adaptive GAN. Specifically, we propose to replace the binary-valued discriminator in a regular generative adversarial network with a novel senone-predicted discriminator, and also introduce a well-designed recognition-aware generator as a dereverberation system. By doing so, the corresponding output distribution will be more suitable for the recognition task. Experimental results on the REVERB corpus show that our proposed approach achieves a relative 18.6% and 8.6% word error rate reduction than the traditional GAN-based baseline system on the simulated set and real set, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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OCLNet: Obfuscation feature Contrastive Learning Network for Weakly Supervised Semantic Segmentation on Ultrasound Images

OCLNet: Obfuscation feature Contrastive Learning Network for...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Jie Zhang, Xianzhi Zhang, Zijian Li, Xuewei Yu, Mei Yu, Ruiguo Liu, Zhiqiang College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China

ISBN: (纸本)9798350368741

Deep learning-based semantic segmentation technology has become a critical tool in assisting doctors with automatic lesion segmentation in medical images. However, the high cost of acquiring large-scale, pixel-level annotations poses a significant challenge, limiting the scalability and application of fully supervised semantic segmentation models. To address this, weakly supervised learning-based semantic segmentation models have emerged as a promising solution. These models can accurately segment lesion regions using only weak annotations, such as image-level or frame-level labels, significantly reducing the annotation burden. This approach has gained substantial attention in current *** various medical imaging modalities, ultrasound imaging stands out as a primary diagnostic tool due to its rapid imaging speed, ease of use, and accessibility. This paper focuses on the study of thyroid ultrasound imaging, aiming to achieve accurate classification of nodule regions. The goal is to provide clinicians with more precise diagnostic information, improving decision-making in thyroid disease diagnosis. © 2025 IEEE.

关键词： Semantic Segmentation Thyroid Nodule Ultrasound Images Weakly Supervised Learning

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Noise-Disentanglement Metric Learning for Robust Speaker Verification 48

Noise-Disentanglement Metric Learning for Robust Speaker Ver...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Sun, Yao Zhang, Hanyi Wang, Longbiao Lee, Kong Aik Liu, Meng Dang, Jianwu Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin China A*STAR Institute for Infocomm Research Singapore

ISBN: (纸本)9781728163277

Automatic speaker verification (ASV) suffers from performance degradation in noisy environments. To solve this problem, we propose the noise-disentanglement metric learning to reduce the speaker-irrelevant noisy components and build a noise-invariant embedding space. Specifically, the disentanglement module, including the speaker encoder and re-construction module, is dedicated to decoupling speech signals. The speaker encoder is used to disentangle speaker-related components, and the reconstruction module increases the model's ability to constrain the noise information by re-constructing the signal. In addition, distribution optimization is introduced to supervise the spatial structure of speaker embeddings under noisy environments. Experiments on Vox-Celeb1 indicate that the proposed method improves the performance of the speaker verification system in both clean and noisy conditions. © 2023 IEEE.

关键词： Embeddings

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Enhancing Multimodal Alignment with Momentum Augmentation for Dense Video Captioning 48

Enhancing Multimodal Alignment with Momentum Augmentation fo...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wei, Yiwei Yuan, Shaozu Chen, Meng Wang, Longbiao Tianjin University College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Jd Ai Research Beijing China

ISBN: (纸本)9781728163277

Dense video captioning aims to localize multiple events from an untrimmed video and generate corresponding captions for each event. Fusing different modalities(e.g. rgb, flow, audio) via transformer structure is a promising way to improve the caption performance. However, it is challenging for the cross-modal encoder to learn multimodal interactions due to their inherent disparities of distribution. In this paper, we propose a novel transformer structure with contrastive learning to align different modalities. Specifically, to avoid the limitation of small batch size and false contrastive targets, we design an event-aligned momentum augmentation strategy to apply contrast learning for dense video captioning. The experimental result shows that our proposals outperform all existing multimodal fusion methods for dense video captioning. © 2023 IEEE.

关键词： Contrastive learning Dense video captioning Momentum augmentation Multimodal fusion

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Continuous Relational Diffusion driven Topic Model with Multi-grained Text for Microblog 30

Continuous Relational Diffusion driven Topic Model with Mult...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wu, Chenhao He, Ruifang Liu, Chang Wang, Bo School of New Media and Communication Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China

ISBN: (纸本)9782493814104

Topic model is a statistical model that leverages unsupervised learning to mine hidden topics in document collections. The data sparsity and colloquialism of social texts make it difficult to accurately mine the topics. Traditional methods assume that there are only 0/1-state relationships between the two parties in the social networks, but the relationship status in real life is more complicated, such as continuously changing relationships with different degrees of intimacy. This paper proposes a continuous relational diffusion driven topic model (CRTM) with multi-grained text for Microblog to realize the continuous representation of the relationship state and make up for the context and structural information lost by previous representation methods. Multi-grained text representation learning distinguishes the impact of formal and informal expression on the topics further and alleviates colloquialism problems. Specifically, based on the original social network, the reconstructed social network with continuous relationship status is obtained by using information diffusion technology. The graph convolution model is utilized to learn node embeddings through the new social network. Finally, the neural variational inference is applied to generate topics according to continuous relationships. We validate CRTM on three real datasets, and the experimental results show the effectiveness of the scheme. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Diffusion

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Leveraging Positional-Related Local-Global Dependency for Synthetic Speech Detection 48

Leveraging Positional-Related Local-Global Dependency for Sy...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Xiaohui Liu, Meng Wang, Longbiao Lee, Kong Aik Zhang, Hanyi Dang, Jianwu Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin China Institute for Infocomm Research A*STAR Singapore

ISBN: (纸本)9781728163277

Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. As synthetic speech exhibits local and global artifacts compared to natural speech, incorporating local-global dependency would lead to better anti-spoofing performance. To this end, we propose the Rawformer that leverages positional-related local-global dependency for synthetic speech detection. The two-dimensional convolution and Transformer are used in our method to capture local and global dependency, respectively. Specifically, we design a novel positional aggregator that integrates local-global dependency by adding positional information and flattening strategy with less information loss. Furthermore, we propose the squeeze-and-excitation Rawformer (SE-Rawformer), which introduces squeeze-and-excitation operation to acquire local dependency better. The results demonstrate that our proposed SE-Rawformer leads to 37% relative improvement compared to the single state-of-the-art system on ASVspoof 2019 LA and generalizes well on ASVspoof 2021 LA. Especially, using the positional aggregator in the SE-Rawformer brings a 43% improvement on average. © 2023 IEEE.

关键词： Signal encoding

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Construction and analysis of Tibetan Khampa dialect corpus for speech synthesis 26

Construction and analysis of Tibetan Khampa dialect corpus f...

引用

26th Conference of the Oriental COCOSDA International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques, O-COCOSDA 2023

作者： Zhu, Yi Lu, Wenhuan Yangzom Hu, Mengfei Khysru, Kuntharrgyal We, Jianguo Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Qinghai Minzu University Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Xining China

ISBN: (纸本)9798350344028

High-quality corpus is the basis for supporting speech synthesis research, and the quality and efficiency of speech synthesis are directly affected by a good or bad speech synthesis corpus. The limited scope of Tibetan language usage and the complexity of phonology and grammar make the resources for this language relatively scarce, leading to a lag in the development of related technology applications. To address this situation, we designed an filtering algorithm based on phoneme balanced Tibetan text data, and selected 9179 Tibetan utterances from a large-scale text database as the text corpus to be used in building the corpus;then, we selected a professional male presenter of the Tibetan Khampa dialect to record the text corpus and build a speech synthesis corpus of about 25 hours in the Tibetan Khampa dialect. The statistical analysis and experimental results show that the synthesis model trained with this corpus can obtain natural and comprehensible speech, which verifies the completeness, validity and usefulness of this corpus. © 2023 IEEE.

关键词： Speech synthesis

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VF-Taco2: Towards Fast and Lightweight Synthesis for Autoregressive Models with Variation Autoencoder and Feature Distillation 48

VF-Taco2: Towards Fast and Lightweight Synthesis for Autoreg...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Yuhao Gong, Cheng Wang, Longbiao Wu, Xixin Liu, Qiuyu Dang, Jianwu Tianjin University College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781728163277

With the development of deep learning, end-to-end neural text-to-speech (TTS) systems have achieved significant improvements in high-quality speech synthesis. However, most of these systems are attention-based autoregressive models, resulting in slow synthesis speed and large model parameter sizes. In this paper, we propose a new fast and lightweight TTS framework named VF-Taco2, which can quickly synthesize speech without GPUs. We first profiled the complexity of decoder process in the current autoregressive model and designed a novel multiple frames prediction module based on variational autoencoder (VAE) to alleviate quality degradation when a larger "reduction factor"is applied. Besides, feature distillation is leveraged to compress a relatively large proposed model to its small version with a minor loss of speech quality. Compared to the original Tacotron 2, our VF-Taco2 achieves a 3.6x-4.4x Mel-spectrum generation acceleration on different performance CPUs, and the parameters are compressed by 1.5x with speech quality maintained. © 2023 IEEE.

关键词： fast knowledge distillation lightweight Speech synthesis VAE

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Time-Domain Speech Enhancement Assisted by Multi-Resolution Frequency Encoder and Decoder 48

Time-Domain Speech Enhancement Assisted by Multi-Resolution ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Shi, Hao Mimura, Masato Wang, Longbiao Dang, Jianwu Kawahara, Tatsuya Kyoto University Graduate School of Informatics Kyoto Japan Tianjin University College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China

ISBN: (纸本)9781728163277

Time-domain speech enhancement (SE) has recently been intensively investigated. Among recent works, DEMUCS [1] introduces multi-resolution STFT loss to enhance performance. However, some resolutions used for STFT contain non-stationary signals, and it is challenging to learn multi-resolution frequency losses simultaneously with only one output. For better use of multi-resolution frequency information, we supplement multiple spectrograms in different frame lengths into the time-domain encoders. They extract stationary frequency information in both narrowband and wideband. We also adopt multiple decoder outputs, each of which computes its corresponding resolution frequency loss. Experimental results show that (1) it is more effective to fuse stationary frequency features than non-stationary features in the encoder, and (2) the multiple outputs consistent with the frequency loss improve performance. Experiments on the Voice-Bank dataset show that the proposed method obtained a 0.14 PESQ improvement. © 2023 IEEE.

关键词： multiresolution spectrograms Speech enhancement time domain

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Stimulus Reconstruction Based Auditory Attention Detection Using EEG in Multi-Speaker Environments Without Access to Clean Sources 19

Stimulus Reconstruction Based Auditory Attention Detection U...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Yang, Kai Luan, Xueying Zhang, Gaoyan Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350346558

Auditory attention detection (AAD) based on electroencephalography (EEG) can be utilized to help hearing-impaired people improve speech perception abilities in multi-speaker environments. Previously, most studies relied on clean speech sources to perform EEG-based AAD, which limits the practical applications in life scenarios with mixed speech and noise. In this paper, we first proposed using a dual-path recurrent neural network (DPRNN) to separate individual speech signals in a multi-speaker scenario and then applied an EEG-based speech reconstruction model by a temporal convolutional network (TCN) to perform AAD. By comparing the correlation of the reconstructed speech envelopes from EEG with the actual ones, the attended side is detected by a higher correlation than the unattended side. Experiments on an EEG dataset with no directional information of speech show that the AAD accuracies of our model are improved for both short and long detection windows (2-s and 5-s) compared with previous leading reconstruction-based AAD methods. Analysis of different frequency bands shows that the contribution of the β band EEG signal to AAD is greatest in both the 2-s and 5-s models. Visualization of the weights suggests that the temporal, frontal, and parietal lobes of the brain contribute most to the model. These findings are consistent with previous studies on the neural mechanism of AAD, which demonstrates the effectiveness and interpretability of the proposed model. © 2022 IEEE.

关键词： Electroencephalography

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