Utterance-Wise Recurrent Dropout And Iterative Speaker Adaptation For Robust Monaural Speech Recognition

【Utterance-Wise Recurrent Dropout And Iterative Speaker Adaptation For Robust Monaural Speech Recognition】的更多相关文章

Utterance-Wise Recurrent Dropout And Iterative Speaker Adaptation For Robust Monaural Speech Recognition

单声道语音识别的逐句循环Dropout迭代说话人自适应 WRBN(wide residual BLSTM network,宽残差双向长短时记忆网络) [2] J. Heymann, L. Drude, and R. Haeb-Umbach, "Wide residual blstm network with discriminative speaker adaptation for robust speech recognition," submitted to the CHi…

A Bayesian Approach to Deep Neural Network Adaptation with Applications to Robust Automatic Speech Recognition

基于贝叶斯的深度神经网络自适应及其在鲁棒自动语音识别中的应用直接贝叶斯DNN自适应使用高斯先验对DNN进行MAP自适应为何贝叶斯在模型自适应中很有用? 因为自适应问题可以视为后验估计问题: 能够克服灾难性遗忘问题在实现通用智能时,神经网络需要学习并记住多个任务,任务顺序无标注,任务会不可预期地切换,同种任务可能在很长一段时间内不会复现.当对当前任务B进行学习时,对先前任务A的知识会突然地丢失,这种现象被称为灾难性遗忘(catastrophic forgetting). DNN的M…

Empirical Evaluation of Speaker Adaptation on DNN based Acoustic Model

DNN声学模型说话人自适应的经验性评估年3月27日发表于:Sound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS) 说话人自适应时从说话人无关模型中估计一个说话人相关的声学模型,以减小训练集与测试集由于说话人差异导致的不匹配. 已经出现了许多DNN自适应方法,但是缺乏实验比较. 声学模型采用TDNN-LSTM声学模型. 自适应源时标准中文普通话声…

(zhuan) Attention in Long Short-Term Memory Recurrent Neural Networks

Attention in Long Short-Term Memory Recurrent Neural Networks by Jason Brownlee on June 30, 2017 in Deep Learning The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitati…

论文翻译：2020_FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective functions

论文地址:FLGCNN:一种新颖的全卷积神经网络,用于基于话语的目标函数的端到端单耳语音增强论文代码:https://github.com/LXP-Never/FLGCCRN(非官方复现) 引用格式:Zhu Y, Xu X, Ye Z. FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective funct…

Recurrent Neural Network[survey]

0.引言我们发现传统的(如前向网络等)非循环的NN都是假设样本之间无依赖关系(至少时间和顺序上是无依赖关系),而许多学习任务却都涉及到处理序列数据,如image captioning,speech synthesis,music generation是基于模型输出序列数据:如time series prediction,video analysis,musical information retrieval是基于模型输入需要序列数据:而如translating natural language…

(zhuan) Recurrent Neural Network

Recurrent Neural Network 2016年07月01日 Deep learning Deep learning 字数:24235 this blog from: http://jxgu.cc/blog/recent-advances-in-RNN.html References Robert Dionne Neural Network Paper Notes Baisc Improvements 20170326 Learning Simpler Language…

深度自适应增量学习（Incremental Learning Through Deep Adaptation）

深度自适应增量学习(Incremental Learning Through Deep Adaptation) 2018-05-25 18:56:00 木呆呆瓶子阅读数 10564 收藏更多分类专栏: 算法学习增量学习版权声明:本文为博主原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接和本声明. 本文链接:https://blog.csdn.net/qq_33880788/article/details/80455714 翻译论文:Incremental L…

Text Prompted Remote Speaker Authentication : Joint Speech and Speaker Recognition/Verification System :: Major Project ::: Introduction

转载自:http://ganeshtiwaridotcomdotnp.blogspot.com/2010/12/text-prompted-remote-speaker.html Biometrics is, in the simplest definition, something you are. It is a physical characteristic unique to each individual such as fingerprint, retina, iris, speec…

论文翻译：2020_WaveCRN: An efficient convolutional recurrent neural network for end-to-end speech enhancement

论文地址:用于端到端语音增强的卷积递归神经网络论文代码:https://github.com/aleXiehta/WaveCRN 引用格式:Hsieh T A, Wang H M, Lu X, et al. WaveCRN: An efficient convolutional recurrent neural network for end-to-end speech enhancement[J]. IEEE Signal Processing Letters, 2020, 27: 2149…