activity
20152023
most citedWaveform Modeling and Generation Using Hierarchical Recurrent Neural Networks for Speech Bandwidth Extension

65 citations · 295 across the 28 of their papers we have counts for

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Showing eess.ASShow all

18 papers · 1 filter

eess.AS2023

CASA-ASR: Context-Aware Speaker-Attributed ASR

Mohan Shi, Zhihao Du, Qian Chen +5

Recently, speaker-attributed automatic speech recognition (SA-ASR) has attracted a wide attention, which aims at answering the question ``who spoke what''. Different from modular s…

eess.AS20231 cited

Semantic VAD: Low-Latency Voice Activity Detection for Speech Interaction

Mohan Shi, Yuchun Shu, Lingyun Zuo +4

For speech interaction, voice activity detection (VAD) is often used as a front-end. However, traditional VAD algorithms usually need to wait for a continuous tail silence to reach…

eess.AS2023

Joint Generative-Contrastive Representation Learning for Anomalous Sound Detection

Xiao-Min Zeng, Yan Song, Zhu Zhuo +5

In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD). GeCo exploits a Predictive AutoEncoder (PAE)…

eess.AS20222 cited

Robust Data2vec: Noise-robust Speech Representation Learning for ASR by Combining Regression and Improved Contrastive Learning

Qiu-Shi Zhu, Long Zhou, Jie Zhang +3

Self-supervised pre-training methods based on contrastive learning or regression tasks can utilize more unlabeled data to improve the performance of automatic speech recognition (A…

eess.AS20228 cited

Joint Training of Speech Enhancement and Self-supervised Model for Noise-robust ASR

Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang +1

Speech enhancement (SE) is usually required as a front end to improve the speech quality in noisy environments, while the enhanced speech might not be optimal for automatic speech…

eess.AS202218 cited

Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals

Xing-Yu Chen, Qiu-Shi Zhu, Jie Zhang +1

In this work, we propose a bi-directional long short-term memory (BiLSTM) network based COVID-19 detection method using breath/speech/cough signals. By using the acoustic signals t…