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检索条件"机构=Dept. of Computer Science and Engineering & MoE Key Lab of AI"

共 651 条记录，以下是11-20 订阅

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Fast-Hubert: an Efficient Training Framework for Self-Supervised Speech Representation Learning

Fast-Hubert: an Efficient Training Framework for Self-Superv...

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Yang, Guanrou Ma, Ziyang Zheng, Zhisheng Song, Yakun Niu, Zhikang Chen, Xie Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering China

ISBN: (纸本)9798350306897

Recent years have witnessed significant advancements in self-supervised learning (SSL) methods for speech-processing tasks. Various speech-based SSL models have been developed and present promising performance on a range of downstream tasks including speech recognition. However, existing speech-based SSL models face a common dilemma in terms of computational cost, which might hinder their potential application and in-dept. academic research. To address this issue, we first analyze the computational cost of different modules during HuBERT pre-training and then introduce a stack of efficiency optimizations, which is named Fast-HuBERT in this paper. The proposed Fast-HuBERT can be trained in 1.1 days with 8 V100 GPUs on the Librispeech 960 h benchmark, without performance degradation, resulting in a 5.2x speedup, compared to the original implementation. Moreover, we explore two well-studied techniques in the Fast-HuBERT and demonstrate consistent improvements as reported in previous work.11The code for Fast-HuBERT training is available at https://***/yanghaha0908/FastHuBERT © 2023 IEEE.

关键词： Speech recognition

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Predictive Skim: Contrastive Predictive Coding for Low-Latency Online Speech Separation 48

Predictive Skim: Contrastive Predictive Coding for Low-Laten...

引用

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

作者： Li, Chenda Wu, Yifei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering China

ISBN: (纸本)9781728163277

In online speech separation, there is a trade-off between inherent latency and speech separation performance. When processing the current input audio, looking ahead to more future context usually brings better speech separation performance but increases the algorithm latency, and vice versa. In the requirements of extremely low latency, the future context is expensive for the algorithm latency and may not be available. In this work, we apply the contrastive predictive coding (CPC) method to the previously proposed online Skipping Memory (SkiM) speech separation model, which is a low-latency model for online speech separation. During the training stage, the SkiM model is required to predict the future memory states given the history memory. By using CPC training, the predictive SkiM model shows stronger causal sequence modeling capacity in the online speech separation task. In addition, we explore a local context codec (LCC) method to reduce the computational cost, and we make qualitative analyses on it. Our best online predictive SkiM equipped with CPC and LCC gets 15.5 dB SI-SNR improvement on WSJ02-mix benchmark with 3-ms actual latency tested on a single-core CPU, which should be the state-of-the-art results among causal models. © 2023 IEEE.

关键词： Economic and social effects

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Advanced Zero-Shot Text-to-Speech for Background Removal and Preservation with Controllable Masked Speech Prediction

Advanced Zero-Shot Text-to-Speech for Background Removal and...

引用

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

作者： Zhang, Leying Zhang, Wangyou Chen, Zhengyang Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

The acoustic background plays a crucial role in natural conversation. It provides context and helps listeners understand the environment, but a strong background makes it difficult for listeners to understand spoken words. The appropriate handling of these backgrounds is situation-dependent: Although it may be necessary to remove background to ensure speech clarity, preserving the background is sometimes crucial to maintaining the contextual integrity of the speech. Despite recent advancements in zero-shot Text-to-Speech technologies, current systems often struggle with speech prompts containing backgrounds. To address these challenges, we propose a Controllable Masked Speech Prediction strategy coupled with a dual-speaker encoder, utilizing a task-related control signal to guide the prediction of dual background removal and preservation targets. Experimental results demonstrate that our approach enables precise control over the removal or preservation of background across various acoustic conditions and exhibits strong generalization capabilities in unseen scenarios. © 2025 IEEE.

关键词： background preservation background removal flow-matching text-to-speech

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Advancing Non-intrusive Suppression on Enhancement Distortion for Noise Robust ASR

Advancing Non-intrusive Suppression on Enhancement Distortio...

引用

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

作者： Wang, Wei Zhao, Siyi Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350368741

Recent advancements in speech enhancement (SE) techniques have greatly improved speech clarity and intelligibility in challenging acoustic environments. However, integrating SE into automatic speech recognition (ASR) systems often results in performance degradation due to artifacts introduced during the enhancement process. While various methods have enhanced recognition accuracy in SE-ASR systems, they often require fine-tuning or re-training of SE or ASR models, which is impractical in many real-world applications. In this paper, we propose a lightweight distortion suppression (DS) network that addresses these artifacts without modifying the SE or ASR models, treating them as fixed black boxes. The DS module operates on the time-frequency (T-F) bands of the original and enhanced complex spectrograms, efficiently compensating for SE distortions using the original T-F information. We validate our approach through experiments on both Mandarin and English ASR tasks using monaural and multi-channel SE frontends, across various ASR backends. Results show that the DS module significantly improves the performance of SE-ASR systems, even when used with robust commercial ASR backends. © 2025 IEEE.

关键词： non-intrusive robust speech recognition speech distortion speech enhancement

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Exploring Time-Frequency Domain Target Speaker Extraction For Causal and Non-Causal Processing

Exploring Time-Frequency Domain Target Speaker Extraction Fo...

引用

2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Zhang, Wangyou Yang, Lei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute Department of Computer Science and Engineering Shanghai China China

ISBN: (纸本)9798350306897

In recent years, target speaker extraction (TSE) has drawn increasing interest as an alternative to speech separation in realistic applications. While time-domain methods have been widely used in recent studies to achieve high performance, the potential of time-frequency (T-F) domain methods have been less explored. In this paper, we try to fill this gap and propose to incorporate the top-performing T-F domain speech separation method into the TSE framework. We first explore different speaker information fusion methods for the proposed model. In addition to the commonly-used concatenation based fusion, we propose a novel speaker token-based fusion method to fuse the target speaker information. Second, we show that the proposed model can be easily extended for causal processing with strong performance. Experiments on the WSJ0-2mix and LibriMix benchmarks show that our proposed model outperforms the widely-used time-domain models in both causal and non-causal settings by a large margin. © 2023 IEEE.

关键词： Extraction

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Robust Audio-Visual ASR with Unified Cross-Modal Attention 48

Robust Audio-Visual ASR with Unified Cross-Modal Attention

引用

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

作者： Li, Jiahong Li, Chenda Wu, Yifei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Audio-visual speech recognition (AVSR) takes advantage of noise-invariant visual information to improve the robustness of automatic speech recognition (ASR) systems. While previous works mainly focused on the clean condition, we believe the visual modality is more effective in noisy environments. The challenges arise from the difficulty of adaptive fusion of audio-visual information and the possible interferences inside the training data. In this paper, we present a new audio-visual speech recognition model with a unified cross-modal attention mechanism. In particular, the auxiliary visual evidence is combined with the acoustic feature along the temporal dimension in the unified space before the deep encoding network. This method provides a flexible cross-modal context and requires no forced alignment such that the model can learn to leverage the audio-visual information in relevant frames. In experiments, the proposed model is demonstrated to be robust to the potential absence of the visual modality or misalignment in audio-visual frames. On the large-scale audio-visual dataset LRS3, our new model further reduces the state-of-The-Art WER for clean utterances and significantly improves the performance under noisy conditions. © 2023 IEEE.

关键词： Speech recognition

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Emodiff: Intensity Controllable Emotional Text-to-Speech with Soft-label Guidance 48

Emodiff: Intensity Controllable Emotional Text-to-Speech wit...

引用

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

作者： Guo, Yiwei Du, Chenpeng Chen, Xie Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Although current neural text-to-speech (TTS) models are able to generate high-quality speech, intensity controllable emotional TTS is still a challenging task. Most existing methods need external optimizations for intensity calculation, leading to suboptimal results or degraded quality. In this paper, we propose EmoDiff, a diffusion-based TTS model where emotion intensity can be manipulated by a proposed soft-label guidance technique derived from classifier guidance. Specifically, instead of being guided with a one-hot vector for the specified emotion, EmoDiff is guided with a soft label where the value of the specified emotion and Neutral is set to α and 1 - α respectively. The α here represents the emotion intensity and can be chosen from 0 to 1. Our experiments show that EmoDiff can precisely control the emotion intensity while maintaining high voice quality. Moreover, diverse speech with specified emotion intensity can be generated by sampling in the reverse denoising process. © 2023 IEEE.

关键词： classifier guidance de-noising diffusion models emotion intensity control Emotional TTS

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DiffVoice: Text-to-Speech with Latent Diffusion 48

DiffVoice: Text-to-Speech with Latent Diffusion

引用

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

作者： Liu, Zhijun Guo, Yiwei Yu, Kai Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-Lance Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

In this work, we present DiffVoice, a novel text-to-speech model based on latent diffusion. We propose to first encode speech signals into a phoneme-rate latent representation with a variational autoencoder enhanced by adversarial training, and then jointly model the duration and the latent representation with a diffusion model. Subjective evaluations on LJSpeech and LibriTTS datasets demonstrate that our method beats the best publicly available systems in naturalness. By adopting recent generative inverse problem solving algorithms for diffusion models, DiffVoice achieves the state-of-the-art performance in text-based speech editing, and zero-shot adaptation. © 2023 IEEE.

关键词： diffusion probabilistic model speech editing speech synthesis variational autoencoder zero-shot adaptation

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Adaptive Large Margin Fine-Tuning For Robust Speaker Verification 48

Adaptive Large Margin Fine-Tuning For Robust Speaker Verific...

引用

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

作者： Zhang, Leying Chen, Zhengyang Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

Large margin fine-tuning (LMFT) is an effective strategy to improve the speaker verification system's performance and is widely used in speaker verification challenge systems. Because the large margin in the loss function could make the training task too difficult, people usually use longer training segments to alleviate this problem in LMFT. However, the LMFT model could have a duration mismatch with the real scenario verification, where the verification speech may be very short. In our experiments, we also find that LMFT fails in short duration and other verification scenarios. To solve this problem, we propose the duration-based and similarity-based adaptive large margin fine-tuning (ALMFT) strategy. To verify its effectiveness, we constructed fixed, variable length, and asymmetric verification trials based on VoxCeleb1. Experimental results demonstrate that ALMFT algorithms are very effective and robust, which not only achieve comparable improvement with LMFT in official VoxCeleb evaluation trials but also overcome performance degradation problems in short-duration and asymmetric scenarios respectively. © 2023 IEEE.

关键词： asymmetric scenario duration mismatch large margin fine-tuning speaker verification

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Factorized AED: Factorized Attention-Based Encoder-Decoder for Text-Only Domain Adaptive ASR 48

Factorized AED: Factorized Attention-Based Encoder-Decoder f...

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

作者： Gong, Xun Wang, Wei Shao, Hang Chen, Xie Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9781728163277

End-to-end automatic speech recognition (ASR) systems have gained popularity given their simplified architecture and promising results. However, text-only domain adaptation remains a big challenge for E2E systems. Text-to-speech (TTS) based approaches fine-tune ASR models by synthesized speech with an auxiliary TTS model, thus increase deployment costs. Language model (LM) fusion based approaches can achieve good performance but are sensitive to interpolation parameters. In order to factorize out the language component in the AED model, we propose the factorized attention-based encoder-decoder (Factorized AED) model whose decoder takes as input the posterior probabilities of a jointly trained LM. Moreover, in the context of domain adaptation, the domain specific LM serves as a plug-and-play component for a well-trained factorized AED model. In-domain experiments on LibriSpeech and out-of-domain experiments adapting from LibriSpeech to a variety of domains in GigaSpeech are conducted to validate the effectiveness of our proposed methods. Results show 20% / 24% relative word error rate (WER) reduction for LibriSpeech test sets and 8 ∼34% relative WER reduction for 8 GigaSpeech target domains test sets compared to the AED baseline. © 2023 IEEE.

关键词： domain adaptation end-to-end speech recognition factorized AED text-only

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