检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

60 篇 会议
14 篇 期刊文献

馆藏范围

74 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

33 篇 工学
- 20 篇 计算机科学与技术...
- 19 篇 软件工程
- 16 篇 信息与通信工程
- 6 篇 生物工程
- 4 篇 生物医学工程（可授...
- 2 篇 仪器科学与技术
- 2 篇 动力工程及工程热...
- 2 篇 控制科学与工程
- 1 篇 光学工程
- 1 篇 电气工程
- 1 篇 安全科学与工程
27 篇 理学
- 18 篇 物理学
- 14 篇 数学
- 9 篇 生物学
- 8 篇 统计学（可授理学、...
- 3 篇 系统科学
9 篇 管理学
- 5 篇 图书情报与档案管...
- 4 篇 管理科学与工程(可...
- 2 篇 工商管理
3 篇 文学
- 2 篇 新闻传播学
- 1 篇 外国语言文学
3 篇 医学
- 3 篇 临床医学
- 2 篇 基础医学(可授医学...
- 1 篇 口腔医学
- 1 篇 公共卫生与预防医...
- 1 篇 特种医学
1 篇 经济学
- 1 篇 应用经济学
1 篇 法学
- 1 篇 法学
1 篇 教育学
- 1 篇 心理学(可授教育学...
1 篇 农学

主题

13 篇 speech recogniti...
8 篇 speech processin...
7 篇 hidden markov mo...
6 篇 natural language...
6 篇 automatic speech...
5 篇 speech
4 篇 noise measuremen...
4 篇 acoustics
4 篇 decoding
4 篇 training
3 篇 support vector m...
3 篇 lattices
3 篇 training data
3 篇 signal processin...
3 篇 vocabulary
3 篇 computational mo...
2 篇 neurons
2 篇 deep learning
2 篇 computer aided l...
2 篇 indexes

机构

5 篇 center for langu...
3 篇 department of el...
3 篇 institute for la...
3 篇 department of el...
2 篇 department of sp...
2 篇 school of electr...
2 篇 department of ps...
2 篇 center for langu...
2 篇 center for langu...
2 篇 department of el...
2 篇 institute for la...
2 篇 electrical engin...
2 篇 computer enginee...
2 篇 speech and langu...
2 篇 center for langu...
2 篇 human language t...
2 篇 center for cogni...
2 篇 center for langu...
2 篇 center for langu...
2 篇 institute for la...

作者

7 篇 sanjeev khudanpu...
6 篇 damianos karakos
5 篇 byrne william
4 篇 drosatos george
4 篇 mounya elhilali
4 篇 efraimidis pavlo...
3 篇 g. cauwenberghs
3 篇 gunawardana asel...
3 篇 khudanpur sanjee...
3 篇 dehak najim
2 篇 sriram ganapathy
2 篇 perifanis vasile...
2 篇 feng siyuan
2 篇 stamatelatos gio...
2 篇 puyang xu
2 篇 abavisani ali
2 篇 kashani hamidrez...
2 篇 scharenborg odet...
2 篇 hermansky hynek
2 篇 t.m. kamm

语言

72 篇 英文
2 篇 其他

检索条件"机构=Center for Language and Speech Processin Department of Electrical and Computer Engineering"

共 74 条记录，以下是41-50 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Multilevel speech intelligibility for robust speaker recognition

Multilevel speech intelligibility for robust speaker recogni...

引用

International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Sridhar Krishna Nemala Mounya Elhilali Department of Electrical and Computer Engineering Center for Speech and Language Processing Johns Hopkins University Baltimore MD USA

In the real world, natural conversational speech is an amalgam of speech segments, silences and environmental/ background and channel effects. Labeling the different regions of an acoustic signal according to their information levels would greatly benefit all automatic speech processing tasks. In the current work, we propose a novel segmentation approach based on a perception-based measure of speech intelligibility. Unlike segmentation approaches based on various forms of voice-activity detection (VAD), the proposed parsing approach exploits higher-level perceptual information about signal intelligibility levels. This labeling information is integrated into a novel multilevel framework for automatic speaker recognition task. The system processes the input acoustic signal along independent streams reflecting various levels of intelligibility and then fusing the decision scores from the multiple steams according to their intelligibility contribution. Our results show that the proposed system achieves significant improvements over standard baseline and VAD-based approaches, and attains a performance similar to the one obtained with oracle speech segmentation information.

关键词： speech Multilevel systems speech recognition Acoustics Speaker recognition speech processing NIST

来源：评论

学校读者我要写书评

暂无评论

Multistream robust speaker recognition based on speech intelligibility

Multistream robust speaker recognition based on speech intel...

引用

Annual Conference on Information Sciences and Systems

作者： Nemala, Sridhar Krishna Elhilali, Mounya Department of Electrical and Computer Engineering Center for Speech and Language Processing Johns Hopkins University Baltimore MD 21218 United States

ISBN: (纸本)9781424498475

Delimiting the most informative voice segments of an acoustic signal is often a crucial initial step for any speech processing system. In the current work, we propose a novel segmentation approach based on a perception-based measure of speech intelligibility. Unlike segmentation approaches based on various forms of voice-activity detection (VAD), the proposed segmentation approach exploits higher-level perceptual information about the signal intelligibility levels. This classification based on intelligibility estimates is integrated into a novel multistream framework for automatic speaker recognition task. The multistream system processes the input acoustic signal along multiple independent streams reflecting various levels of intelligibility and then fusing the decision scores from the multiple steams according to their intelligibility contribution. Our results show that the proposed multistream system achieves significant improvements both in clean and noisy conditions when compared with a baseline and a state-of-the-art voice-activity detection algorithm. © 2011 IEEE.

关键词： speech intelligibility

来源：评论

学校读者我要写书评

暂无评论

Randomized maximum entropy language models

Randomized maximum entropy language models

引用

2011 IEEE Workshop on Automatic speech Recognition and Understanding, ASRU 2011

作者： Xu, Puyang Khudanpur, Sanjeev Gunawardana, Asela Department of Electrical and Computer Engineering Center of Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Microsoft Research Redmond WA 98052 United States

ISBN: (纸本)9781467303675

We address the memory problem of maximum entropy language models(MELM) with very large feature sets. Randomized techniques are employed to remove all large, exact data structures in MELM implementations. To avoid the dictionary structure that maps each feature to its corresponding weight, the feature hashing trick [1] [2] can be used. We also replace the explicit storage of features with a Bloom filter. We show with extensive experiments that false positive errors of Bloom filters and random hash collisions do not degrade model performance. Both perplexity and WER improvements are demonstrated by building MELM that would otherwise be prohibitively large to estimate or store. © 2011 IEEE.

关键词： Data structures

来源：评论

学校读者我要写书评

暂无评论

Efficient subsampling for training complex language models

Efficient subsampling for training complex language models

引用

Conference on Empirical Methods in Natural language processing, EMNLP 2011

作者： Xu, Puyang Gunawardana, Asela Khudanpur, Sanjeev Department of Electrical and Computer Engineering Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218 United States Microsoft Research Redmond WA 98052 United States

ISBN: (纸本)1937284115

We propose an efficient way to train maximum entropy language models (MELM) and neural network language models (NNLM). The advantage of the proposed method comes from a more robust and efficient subsampling technique. The original multi-class language modeling problem is transformed into a set of binary problems where each binary classifier predicts whether or not a particular word will occur. We show that the binarized model is as powerful as the standard model and allows us to aggressively subsample negative training examples without sacrificing predictive performance. Empirical results show that we can train MELM and NNLM at 1% ∼ 5% of the standard complexity with no loss in performance. © 2011 Association for Computational Linguistics.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

Radial Gaussianization with cluster-specific bias compensation

Radial Gaussianization with cluster-specific bias compensati...

引用

IEEE/SP Workshop on Statistical Signal processing (SSP)

作者： Shuai Huang Damianos Karakos Daguang Xu Center of Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Human Language Technology Center of Excellence Center of Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

In recent work, Lyu and Simoncelli [1] introduced radial Gaussianization (RG) as a very efficient procedure for transforming n-dimensional random vectors into Gaussian vectors with independent and identically distributed (i.i.d.) components. This entails transforming the norms of the data so that they become chi-distributed with n degrees of freedom. A necessary requirement is that the original data are generated by an isotropic distribution, that is, their probability density function (pdf) is constant over surfaces of n-dimensional spheres (or, more general, n-dimensional ellipsoids). The case of biases in the data, which is of great practical interest, is studied here; as we demonstrate with experiments, there are situations in which even very small amounts of bias can cause RG to fail. This becomes evident especially when the data form clusters in low-dimensional manifolds. To address this shortcoming, we propose a two-step approach which entails (i) first discovering clusters in the data and removing the bias from each, and (ii) performing RG on the bias-compensated data. In experiments with synthetic data, the proposed bias compensation procedure results in significantly better Gaus-sianization than the non-compensated RG method.

关键词： Principal component analysis Noise measurement Estimation Gaussian distribution Distributed databases Optimization Transforms

来源：评论

学校读者我要写书评

暂无评论

MULTILAYER PERCEPTRON WITH SPARSE HIDDEN OUTPUTS FOR PHONEME RECOGNITION

MULTILAYER PERCEPTRON WITH SPARSE HIDDEN OUTPUTS FOR PHONEME...

引用

IEEE International Conference on Acoustics, speech and Signal processing

作者： G.S.V.S. Sivaram Hynek Hermansky Department of Electrical & Computer Engineering Center of Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University USA

This paper introduces the sparse multilayer perceptron (SMLP) which learns the transformation from the inputs to the targets as in multilayer perceptron (MLP) while the outputs of one of the internal hidden layers is forced to be sparse. This is achieved by adding a sparse regularization term to the cross-entropy cost and learning the parameters of the network to minimize the joint cost. On the TIMIT phoneme recognition task, the SMLP based system trained using perceptual linear prediction (PLP) features performs better than the conventional MLP based system. Furthermore, their combination yields a phoneme error rate of 21.2percent, a relative improvement of 6.2percent over the baseline.

关键词： Hidden Markov models Neurons Training Cost function speech speech processing Acoustics

来源：评论

学校读者我要写书评

暂无评论

Multistream robust speaker recognition based on speech intelligibility

Multistream robust speaker recognition based on speech intel...

引用

Annual Conference on Information Sciences and Systems (CISS)

作者： Sridhar Krishna Nemala Mounya Elhilali Department of Electrical and Computer Engineering Center for Speech and Language Processing Johns Hopkins University Baltimore MD USA

关键词： Computational modeling Robustness Noise

来源：评论

学校读者我要写书评

暂无评论

Randomized maximum entropy language models

Randomized maximum entropy language models

引用

IEEE Workshop on Automatic speech Recognition and Understanding

作者： Puyang Xu Sanjeev Khudanpur Asela Gunawardana Department of Electrical & Computer Engineering Center of Language and Speech Processing Johns Hopkins University Baltimore MD USA Microsoft Research Redmond WA USA

We address the memory problem of maximum entropy language models (MELM) with very large feature sets. Randomized techniques are employed to remove all large, exact data structures in MELM implementations. To avoid the dictionary structure that maps each feature to its corresponding weight, the feature hashing trick [1] [2] can be used. We also replace the explicit storage of features with a Bloom filter. We show with extensive experiments that false positive errors of Bloom filters and random hash collisions do not degrade model performance. Both perplexity and WER improvements are demonstrated by building MELM that would otherwise be prohibitively large to estimate or store.

关键词： Vectors Training Computational modeling Entropy Dictionaries Memory management Vocabulary

来源：评论

学校读者我要写书评

暂无评论

HYPOTHESIS RANKING AND TWO-PASS APPROACHES FOR MACHINE TRANSLATION SYSTEM COMBINATION

HYPOTHESIS RANKING AND TWO-PASS APPROACHES FOR MACHINE TRANS...

引用

IEEE International Conference on Acoustics, speech, and Signal processing

作者： Damianos Karakos Jason Smith Sanjeev Khudanpur Center for Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD 21218 Center for Language and Speech Processing Department of Computer Science Johns Hopkins University Baltimore MD 21218

ISBN: (纸本)9781424442959

Given a number of machine translations of a source segment, the goal of system combination is to produce a new translation that has better quality than all of them. This paper describes a number of improvements that were recently added to the JHU system combination scheme: (i) A hypothesis ranking technique which orders the system outputs, on a per-segment basis, according to predicted translation quality, thus improving a subsequent incremental combination step. (ii) A two-pass combination procedure, which first produces several combination outputs with the given translations, and then performs one more combination step with these new outputs. Results from the NIST MT09 informal system combination evaluation on Arabic-to-English and Urdu-to-English show that both approaches offer significant BLEU and TER gains over a baseline JHU combination scheme.

关键词： Machine Translation System Combination Confusion Network Decoding Hypothesis Ranking

来源：评论

学校读者我要写书评

暂无评论

On projections of Gaussian distributions using maximum likelihood criteria

On projections of Gaussian distributions using maximum likel...

引用

Information Theory and Applications Workshop, ITA

作者： Haolang Zhou Damianos Karakos Sanjeev Khudanpur Andreas G. Andreou Carey E. Priebe Department of Electrical and Computer Engineering and Center of Language and Speech Processing Johns Hopkins University Baltimore MD USA Department of Electrical and Computer Engineering Center for Language and Speech Processing Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781424439904

Generative statistical models with a very large number of parameters are frequently used in real-world data applications, such as large-vocabulary speech recognition (LVCSR). Complex models are needed in order to capture the ubiquitous variability in the observed signal, but data sparsity causes significant problems in their training. One way of dealing with data sparsity is to perform dimensionality reduction of the observed features, with the goal of reducing the model parameter space without sacrificing performance. When the data are Gaussian distributed, the dimensionality reduction can be done efficiently using the maximum likelihood criterion; this leads to the heteroscedastic linear discriminant analysis (HLDA), which is a natural extension of linear discriminant analysis (LDA) to the case where the class-conditional Gaussians have unequal covariance matrices. A further extension of HLDA to multiple transforms (MLDA) can also be tackled efficiently. This paper presents the theory behind HLDA and MLDA, and demonstrates their performance with synthetic data.

关键词： Gaussian distribution Linear discriminant analysis Covariance matrix speech recognition Maximum likelihood estimation Mathematics Acoustics Training data Mel frequency cepstral coefficient Distributed computing

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共8页 << < 1 2 3 4 5 6 7 8 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：