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20192026
most citedA Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition

35 citations · 68 across the 9 of their papers we have counts for

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5 papers · 1 filter

eess.AS2026

Beyond Speech: Dual-Domain SSL Fusion for Unified All-Type Audio Deepfake Detection

Cunhang Fan, Junqin Cao, Tian Gao +4

Unified all-type audio deepfake detection aims to determine whether an input clip is real or fake when its audio type may be speech, environmental sound, singing voice, or music. E…

eess.AS2023

AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer

Kang Li, Yan Song, Li-Rong Dai +3

In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio t…

eess.AS2022★ 35 cited

A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition

Qiu-Shi Zhu, Jie Zhang, Zi-Qiang Zhang +3

Wav2vec2.0 is a popular self-supervised pre-training framework for learning speech representations in the context of automatic speech recognition (ASR). It was shown that wav2vec2.…

eess.AS2021★ 8 cited

XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition

Zi-Qiang Zhang, Yan Song, Ming-Hui Wu +2

In this paper, we propose a weakly supervised multilingual representation learning framework, called cross-lingual self-training (XLST). XLST is able to utilize a small amount of a…

eess.AS2019★ 3 cited

Channel adversarial training for cross-channel text-independent speaker recognition

Xin Fang, Liang Zou, Jin Li +2

The conventional speaker recognition frameworks (e.g., the i-vector and CNN-based approach) have been successfully applied to various tasks when the channel of the enrolment datase…