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- HuBERT：基于BERT的自监督 (self-supervised)语音表示学习
本文我将介绍HuBERT，一个基于BERT的自监督语音表示学习的工作。近年来基于自监督的表示学习在NLP领域非常流行，但语音的表示学习和NLP不同面临很多其它的挑战。HuBERT来自Facebook，在ASR上有非常不错的表现，是…
- Wav2Vec HuBert WavLM 自监督语音大模型 - CSDN博客
腾讯游戏知几AI团队与西工大ASLP组联合发布了基于 WenetSpeech 1 万小时数据训练的中文版 Wav2vec 2 0 和 HuBERT 模型。Wav2vec 2 0 [1]，HuBERT [2] 和 WavLM [3] 等语音预训练模型，通过在多达上万小时的无标注语音数据（如 Libri-light ）上的自监督学习，显著提升了自动语音识别（Automatic Speech Recognition, ASR），语音
- HuBERT: Self-Supervised Speech Representation Learning by Masked . . .
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach
- GitHub - bshall hubert: HuBERT content encoders for: A Comparison of . . .
HuBERT content encoders for: A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion - bshall hubert
- 如何解读huBERT这篇顶会文章？ - 知乎
摘要 论文提出了一种名为HuBERT（Hidden-Unit BERT）的自监督语音表示学习方法，通过掩码预测隐藏单元的聚类标签，解决了语音信号中的三个核心问题：（1）输入语句中多音素共存；（2）预训练阶段缺乏音素词典；（3）音素边界不明确且长度可变。HuBERT采用离线聚类（如k-means）生成对齐的伪标签
- 语音识别预训练模型Hidden-Unit BERT (HuBERT) - CSDN博客
1 简介 本文根据2021年《HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units》翻译总结的。 自监督语音学习面临3个挑战，1）在每句话中有多个声音单元；2）在预训练阶段没有输入声音单元对应的词典；3）声音单元长度可变，没有明确的分割。
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