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1st IEEE Global Emerging Technology Blockchain Forum: Blockchain and Beyond, iGETblockchain 2022

作者： Zikos, Ioannis Sendros, Andreas Drosatos, George Efraimidis, Pavlos S. Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Athena Research Center Institute for Language and Speech Processing Xanthi67100 Greece

ISBN: (纸本)9781665451987

Hyperledger Fabric is an open-source private permissioned blockchain that supports the use of smart contracts (chaincode). It is aimed mainly at private networks of companies. To serve the different needs of each company and to be flexible in customer requirements, it consists of various adaptive components. Although this structure efficiently addresses a wide range of needs, deploying such a network for research purposes or rapid development is complex. In this paper, we present a web-based system architecture for the automated deployment of a Hyperledger Fabric network, and in addition, we describe the tools needed to manage and update such a network. Finally, as a proof-of-concept, we implement the proposed architecture to demonstrate the feasibility of our approach. © 2022 IEEE.

关键词： Blockchain

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EDSep: An Effective Diffusion-Based Method for speech Source Separation

EDSep: An Effective Diffusion-Based Method for Speech Source...

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2025 IEEE International Conference on Acoustics, speech, and Signal processing, ICASSP 2025

作者： Dong, Jinwei Wang, Xinsheng Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China Audio Speech and Language Processing Group School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350368741

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy. © 2025 IEEE.

关键词： diffusion score matching speech separation stochastic differential equation

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APCodec: A Neural Audio Codec with Parallel Amplitude and Phase Spectrum Encoding and Decoding

arXiv

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arXiv 2024年

作者： Ai, Yang Jiang, Xiao-Hang Lu, Ye-Xin Du, Hui-Peng Ling, Zhen-Hua National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei230027 China

This paper introduces a novel neural audio codec targeting high waveform sampling rates and low bitrates named APCodec, which seamlessly integrates the strengths of parametric codecs and waveform codecs. The APCodec revolutionizes the process of audio encoding and decoding by concurrently handling the amplitude and phase spectra as audio parametric characteristics like parametric codecs. It is composed of an encoder and a decoder with the modified ConvNeXt v2 network as the backbone, connected by a quantizer based on the residual vector quantization (RVQ) mechanism. The encoder compresses the audio amplitude and phase spectra in parallel, amalgamating them into a continuous latent code at a reduced temporal resolution. This code is subsequently quantized by the quantizer. Ultimately, the decoder reconstructs the audio amplitude and phase spectra in parallel, and the decoded waveform is obtained by inverse short-time Fourier transform. To ensure the fidelity of decoded audio like waveform codecs, spectral-level loss, quantization loss, and generative adversarial network (GAN) based loss are collectively employed for training the APCodec. To support low-latency streamable inference, we employ feed-forward layers and causal deconvolutional layers in APCodec, incorporating a knowledge distillation training strategy to enhance the quality of decoded audio. Experimental results confirm that our proposed APCodec can encode 48 kHz audio at bitrate of just 6 kbps, with no significant degradation in the quality of the decoded audio. At the same bitrate, our proposed APCodec also demonstrates superior decoded audio quality and faster generation speed compared to well-known codecs, such as Encodec, AudioDec and DAC. Copyright © 2024, The Authors. All rights reserved.

关键词： Vector quantization

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On the Evaluation Metrics for Paraphrase Generation

On the Evaluation Metrics for Paraphrase Generation

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2022 Conference on Empirical Methods in Natural language processing, EMNLP 2022

作者： Shen, Lingfeng Liu, Lemao Jiang, Haiyun Shi, Shuming Department of Computer Science Johns Hopkins University United States Natural Language Processing Center Tencent AI Lab

In this paper we revisit automatic metrics for paraphrase evaluation and obtain two findings that disobey conventional wisdom: (1) Reference-free metrics achieve better performance than their reference-based counterparts. (2) Most commonly used metrics do not align well with human annotation. Underlying reasons behind the above findings are explored through additional experiments and in-depth analyses. Based on the experiments and analyses, we propose ParaScore, a new evaluation metric for paraphrase generation. It possesses the merits of reference-based and reference-free metrics and explicitly models lexical divergence. Based on our analysis and improvements, our proposed reference-based outperforms than reference-free metrics. Experimental results demonstrate that ParaScore significantly outperforms existing metrics. Our codes and toolkit are released in https://***/shadowkiller33/ParaScore. © 2022 Association for Computational Linguistics.

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The NPU-HWC System for the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge

The NPU-HWC System for the ISCSLP 2024 Inspirational and Con...

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International Symposium on Chinese Spoken language processing

作者： Dake Guo Jixun Yao Xinfa Zhu Kangxiang Xia Zhao Guo Ziyu Zhang Yao Wang Jie Liu Lei Xie Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi'an Huawei Cloud

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

This paper presents the NPU-HWC system submitted to the ISCSLP 2024 Inspirational and Convincing Audio Generation Challenge 2024 (ICAGC). Our system consists of two modules: a speech generator for Track 1 and a background audio generator for Track 2. In Track 1, we employ Single-Codec to tokenize the speech into discrete tokens and use a language-model-based approach to achieve zero-shot speaking style cloning. The Single-Codec effectively decouples timbre and speaking style at the token level, reducing the acoustic modeling burden on the autoregressive language model. Additionally, we use DSPGAN to upsample 16 kHz mel-spectrograms to high-fidelity 48 kHz waveforms. In Track 2, we propose a background audio gen-erator based on large language models (LLMs). This system produces scene-appropriate accompaniment descriptions, synthesizes background audio with Tango 2, and integrates it with the speech generated by our Track 1 system. Our submission achieves the second place and the first place in Track 1 and Track 2 respectively.

关键词： Vocoders Large language models Cloning speech Generators Acoustics Timbre speech processing Load modeling

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Development of HMM Based Parts of speech Tagger for Hadoti 1st

Development of HMM Based Parts of Speech Tagger for Hadoti

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Nagar, Anushka Joshi, Nisheeth Katyayan, Pragya Arora, Palak Department of Computer Science Banasthali Vidyapith Rajasthan Radha Kishnpura India Speech and Language Processing Lab Center for Artificial Intelligence Banasthali Vidyapith Rajasthan Radha Kishnpura India

ISBN: (纸本)9783031714832

In this paper, we have shown the development of a Part of speech (POS) tagger for Hadoti - a prominent language spoken in Rajasthan, India - despite its limited resources. For this, we manually tagged a corpus of 50,000 POS-tagged sentences and trained it using a Hidden Markov Model (HMM). Since no prior work had been reported in this area, we couldn't compare our results to any other system. This paper documents the efforts made to create an HMM POS tagger for Hadoti, to stimulate further research in this field. This work is expected to serve as a foundation for preserving the language and as a resource for aspiring researchers who wish to explore this area of Hadoti language processing. The system was evaluated for accuracy and produced 99.87% accurate results on seen data and 98.78% on unseen data. The system was able to produce an accuracy of 99.33% on the entire test corpus. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hidden Markov models

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Towards High-Quality and Efficient speech Bandwidth Extension with Parallel Amplitude and Phase Prediction

arXiv

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arXiv 2024年

作者： Lu, Ye-Xin Ai, Yang Du, Hui-Peng Ling, Zhen-Hua National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei230027 China

speech bandwidth extension (BWE) refers to widening the frequency bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller. This paper proposes a generative adversarial network (GAN) based BWE model with parallel prediction of Amplitude and Phase spectra, named AP-BWE, which achieves both high-quality and efficient wideband speech waveform generation. The proposed AP-BWE generator is entirely based on convolutional neural networks (CNNs). It features a dual-stream architecture with mutual interaction, where the amplitude stream and the phase stream communicate with each other and respectively extend the high-frequency components from the source narrowband amplitude and phase spectra. To improve the naturalness of the extended speech signals, we employ a multi-period discriminator at the waveform level and design a pair of multi-resolution amplitude and phase discriminators at the spectral level, respectively. Experimental results demonstrate that our proposed AP-BWE achieves state-of-the-art performance in terms of speech quality for BWE tasks targeting sampling rates of both 16 kHz and 48 kHz. In terms of generation efficiency, due to the all-convolutional architecture and all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1 times faster than real-time on a single CPU. Notably, to our knowledge, AP-BWE is the first to achieve the direct extension of the high-frequency phase spectrum, which is beneficial for improving the effectiveness of existing BWE methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Discriminators

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CAMEL: Cross-Attention Enhanced Mixture-of-Experts and language Bias for Code-Switching speech Recognition

CAMEL: Cross-Attention Enhanced Mixture-of-Experts and Langu...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： He Wang Xucheng Wan Naijun Zheng Kai Liu Huan Zhou Guojian Li Lei Xie Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi’an China IT Innovation and Research Center Huawei Technologies Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-switching automatic speech recognition (ASR) aims to transcribe speech that contains two or more languages accurately. To better capture language-specific speech representations and address language confusion in code-switching ASR, the mixture-of-experts (MoE) architecture and an additional language diarization (LD) decoder are commonly employed. However, most researches remain stagnant in simple operations like weighted summation or concatenation to fuse language-specific speech representations, leaving significant opportunities to explore the enhancement of integrating language bias information. In this paper, we introduce CAMEL, a cross-attention-based MoE and language bias approach for code-switching ASR. Specifically, after each MoE layer, we fuse language-specific speech representations with cross-attention, leveraging its strong contextual modeling abilities. Additionally, we design a source attention-based mechanism to incorporate the language information from the LD decoder output into text embeddings. Experimental results demonstrate that our approach achieves state-of-the-art performance on the SEAME, ASRU200, and ASRU700+Librispeech460 Mandarin-English code-switching ASR datasets.

关键词： speech coding Fuses speech enhancement Signal processing Logic gates Acoustics Decoding Multilingual Context modeling Automatic speech recognition

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Aligning Noisy-Clean speech Pairs at Feature and Embedding Levels for Learning Noise-Invariant Speaker Representations

Aligning Noisy-Clean Speech Pairs at Feature and Embedding L...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Zuoliang Li Yang Ai Jie Zhang Shengyu Peng Yu Guan Bin Gu Wu Guo The National Engineering Research Center for Speech and Language Information Processing (NERC-SLIP) University of Science and Technology of China (USTC) Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In this paper, we propose a noise-invariant speaker representation learning (SRL) approach by aligning noisy-clean speech pairs at both the feature and embedding levels for model training. Specifically, we first construct noisy-clean pairs using data augmentation during training. The noisy features are then processed by a Conformer-based enhancement module. The feature-level alignment is achieved by minimizing the mean squared error between the enhanced and original clean data. At the embedding level, we introduce a supervised contrastive learning loss with noise-adaptive margin to simultaneously enhance the intra-speaker compactness and the inter-speaker separability and better adapt different noise levels, in combination with the Barlow Twins self-supervised loss to align the noisy-clean data pairs and reduce noise redundancy in the embedding space. Finally, these loss components are integrated with conventional classification loss to train the SRL network. Experimental results on various VoxCeleb1 test sets synthesized with noise sources demonstrate the effectiveness of the proposed method.

关键词： Training Representation learning Noise Redundancy Contrastive learning Feature extraction Data augmentation Noise measurement speech processing Noise level

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ZSVC: Zero-shot Style Voice Conversion with Disentangled Latent Diffusion Models and Adversarial Training

ZSVC: Zero-shot Style Voice Conversion with Disentangled Lat...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Xinfa Zhu Lei He Yujia Xiao Xi Wang Xu Tan Sheng Zhao Lei Xie Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xian China Microsoft Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Style voice conversion aims to transform the speaking style of source speech into a desired style while keeping the original speaker’s identity. However, previous style voice conversion approaches primarily focus on well-defined domains such as emotional aspects, limiting their practical applications. In this study, we present ZSVC, a novel Zero-shot Style Voice Conversion approach that utilizes a speech codec and a latent diffusion model with speech prompting mechanism to facilitate in-context learning for speaking style conversion. To disentangle speaking style and speaker timbre, we introduce information bottleneck to filter speaking style in the source speech and employ Uncertainty Modeling Adaptive Instance Normalization (UMAdaIN) to perturb the speaker timbre in the style prompt. Moreover, we propose a novel adversarial training strategy to enhance in-context learning and improve style similarity. Experiments conducted on 44,000 hours of speech data demonstrate the superior performance of ZSVC in generating speech with diverse speaking styles in zero-shot scenarios.

关键词： Training speech codecs Adaptation models Uncertainty speech coding Transforms speech Diffusion models Information filters Timbre

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