Generalized latent variable models with non-linear effects下载

weixin_39820835 2019-09-19 05:30:33

Generalized latent variable models with non-linear effects
相关下载链接：//download.csdn.net/download/qq_27975367/9369267?utm_source=bbsseo

...全文

40 回复打赏收藏转发到动态举报

写回复

用AI写文章

切换为时间正序

请发表友善的回复…

发表回复

Generalized latent variable models with non-linear effects

Advanced Data Analysis from an Elementary Point of View

MSR Identity Toolbox: A Matlab Toolbox for Speaker Recognition Research Version 1.0 Seyed Omid Sadjadi, Malcolm Slaney, and Larry Heck Microsoft Research, Conversational Systems Research Center (CSRC) s.omid.sadjadi@gmail.com, {mslaney,larry.heck}@microsoft.com This report serves as a user manual for the tools available in the Microsoft Research (MSR) Identity Toolbox. This toolbox contains a collection of Matlab tools and routines that can be used for research and development in speaker recognition. It provides researchers with a test bed for developing new front-end and back-end techniques, allowing replicable evaluation of new advancements. It will also help newcomers in the field by lowering the “barrier to entry”, enabling them to quickly build baseline systems for their experiments. Although the focus of this toolbox is on speaker recognition, it can also be used for other speech related applications such as language, dialect and accent identification. In recent years, the design of robust and effective speaker recognition algorithms has attracted significant research effort from academic and commercial institutions. Speaker recognition has evolved substantially over the past 40 years; from discrete vector quantization (VQ) based systems to adapted Gaussian mixture model (GMM) solutions, and more recently to factor analysis based Eigenvoice (i-vector) frameworks. The Identity Toolbox provides tools that implement both the conventional GMM-UBM and state-of-the-art i-vector based speaker recognition strategies. A speaker recognition system includes two primary components: a front-end and a back-end. The front-end transforms acoustic waveforms into more compact and less redundant representations called acoustic features. Cepstral features are most often used for speaker recognition. It is practical to only retain the high signal-to-noise ratio (SNR) regions of the waveform, therefore there is also a need for a speech activity detector (SAD) in the fr

9.3 广义线性模型(Generalized linear models,缩写为GLMs) 线性回归和逻辑回归都属于广义线性模型的特例(McCullagh and Nelder 1989). 这些模型中输出密度都是指数族分布(参考本书9.2),而均值参数都是输入的线性组合,经过可能是非线性的函数,比如逻辑函数等等.下面就要详细讲一下广义线性模型(GLMs).为了记号简单,先看标量输出的情况.(这就排除了多远逻辑回归,不过这只是为表述简单而已.) 9.3.1 基础知识要理解广义线性模型,首先要考虑一个标量响

RESEARCH TRACK PAPERS - ORAL Title & Authors NetCycle: Collective Evolution Inference in Heterogeneous Information NetworksAuthor(s): Yizhou Zhang*, Fudan University; Xiong Yun, ; Xiangnan Ko...

下载资源悬赏专区

13,656

社区成员

12,674,803

社区内容

发帖

与我相关

我的任务

其他技术论坛（原bbs）

社区管理员

加入社区

近7日
近30日
至今

加载中

查看更多榜单

社区公告

暂无公告

试试用AI创作助手写篇文章吧

+ 用AI写文章