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

153 篇 会议
85 篇 期刊文献

馆藏范围

238 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

164 篇 工学
- 120 篇 计算机科学与技术...
- 106 篇 软件工程
- 46 篇 信息与通信工程
- 14 篇 控制科学与工程
- 12 篇 电气工程
- 11 篇 电子科学与技术（可...
- 9 篇 生物工程
- 8 篇 机械工程
- 8 篇 生物医学工程（可授...
- 6 篇 化学工程与技术
- 5 篇 光学工程
- 2 篇 力学（可授工学、理...
- 2 篇 动力工程及工程热...
109 篇 理学
- 77 篇 物理学
- 37 篇 数学
- 19 篇 统计学（可授理学、...
- 13 篇 系统科学
- 11 篇 生物学
- 6 篇 化学
19 篇 管理学
- 12 篇 图书情报与档案管...
- 5 篇 管理科学与工程(可...
- 3 篇 工商管理
- 3 篇 公共管理
9 篇 医学
- 8 篇 临床医学
- 5 篇 基础医学(可授医学...
- 4 篇 公共卫生与预防医...
- 2 篇 药学(可授医学、理...
4 篇 法学
- 4 篇 社会学
4 篇 教育学
- 2 篇 心理学(可授教育学...
1 篇 哲学
- 1 篇 哲学
1 篇 经济学
1 篇 文学
1 篇 历史学
1 篇 农学
1 篇 艺术学

主题

53 篇 speech recogniti...
15 篇 training
14 篇 hidden markov mo...
13 篇 neural machine t...
12 篇 machine translat...
12 篇 transducers
11 篇 computer aided l...
11 篇 decoding
9 篇 error analysis
9 篇 recurrent neural...
8 篇 speech
8 篇 feature extracti...
8 篇 neural network
7 篇 modelling langua...
7 篇 humans
6 篇 signal processin...
6 篇 vocabulary
6 篇 handwriting reco...
5 篇 hierarchical sys...
5 篇 modeling languag...

机构

40 篇 human language t...
38 篇 apptek gmbh aach...
32 篇 human language t...
20 篇 human language t...
10 篇 human language t...
9 篇 human language t...
8 篇 computer science...
8 篇 human language t...
7 篇 spoken language ...
7 篇 apptek gmbh aach...
6 篇 human language t...
6 篇 apptek gmbh
6 篇 human language t...
5 篇 human language t...
5 篇 human language t...
4 篇 human language t...
3 篇 human language t...
3 篇 informatics inst...
3 篇 rwth aachen univ...
3 篇 limsi cnrs spoke...

作者

141 篇 ney hermann
58 篇 schlüter ralf
37 篇 hermann ney
16 篇 zeyer albert
16 篇 zhou wei
14 篇 gao yingbo
14 篇 ralf schlüter
14 篇 ralf schluter
12 篇 mansour saab
12 篇 zeineldeen moham...
12 篇 michel wilfried
12 篇 zens richard
11 篇 herold christian
10 篇 bahar parnia
10 篇 peitz stephan
9 篇 peter jan-thorst...
9 篇 schluter ralf
9 篇 freitag markus
9 篇 yang zijian
9 篇 wang weiyue

语言

236 篇 英文
2 篇 其他

检索条件"机构=Human Language Technology and Pattern Recognition Group Computer Science Department"

共 238 条记录，以下是61-70 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：

Improved strategies for a zero oov rate LVCSR system 40

Improved strategies for a zero oov rate LVCSR system

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Shaik, M. Ali Basha Mousa, Amr El-Desoky Hahn, Stefan Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition - Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781467369978

In this work, multiple hierarchical language modeling strategies for a zero OOV rate large vocabulary continuous speech recognition system are investigated. In our previously proposed hierarchical approach, a full-word language model and a context independent character-level LM (CLM) are directly used during search. The novelty of this work is to jointly model the character-level prior and the pronunciation probabilities, to introduce across-word context into the characterlevel LM, and to properly normalize the character-level LM using prefix-tree based normalization for the hierarchical approach. Significant reductions in-terms of word error rates (WER) on the best full-word Quaero Polish LVCSR system are reported. © 2015 IEEE.

关键词： hierarchical LVCSR OOV prefix-tree

来源：评论

学校读者我要写书评

暂无评论

Investigations on sequence training of neural networks 40

Investigations on sequence training of neural networks

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Wiesler, Simon Golik, Pavel Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

In this paper we present an investigation of sequence-discriminative training of deep neural networks for automatic speech recognition. We evaluate different sequence-discriminative training criteria (MMI and MPE) and optimization algorithms (including SGD and Rprop) using the RASR toolkit. Further, we compare the training of the whole network with that of the output layer only. Technical details necessary for a robust training are studied, since there is no consensus yet on the ultimate training recipe. The investigation extends our previous work on training linear bottleneck networks from scratch showing the consistently positive effect of sequence training. © 2015 IEEE.

关键词： deep neural networks optimization sequence training speech recognition

来源：评论

学校读者我要写书评

暂无评论

Sequence-discriminative training of recurrent neural networks 40

Sequence-discriminative training of recurrent neural network...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Voigtlaender, Paul Doetsch, Patrick Wiesler, Simon Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany LIMSI CNRS Spoken Language Processing Group Paris France

ISBN: (纸本)9781467369978

We investigate sequence-discriminative training of long shortterm memory recurrent neural networks using the maximum mutual information criterion. We show that although recurrent neural networks already make use of the whole observation sequence and are able to incorporate more contextual information than feed forward networks, their performance can be improved with sequence-discriminative training. Experiments are performed on two publicly available handwriting recognition tasks containing English and French handwriting. On the English corpus, we obtain a relative improvement in WER of over 11% with maximum mutual information (MMI) training compared to cross-entropy training. On the French corpus, we observed that it is necessary to interpolate the MMI objective function with cross-entropy. © 2015 IEEE.

关键词： handwriting recognition long shortterm memory recurrent neural networks sequence-discriminative training

来源：评论

学校读者我要写书评

暂无评论

Feature-rich sub-lexical language models using a maximum entropy approach for German LVCSR

Feature-rich sub-lexical language models using a maximum ent...

引用

14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013

作者： Shaik, M. Ali Basha El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

German is a morphologically rich language having a high degree of word inflections, derivations and compounding. This leads to high out-of-vocabulary (OOV) rates and poor language model (LM) probabilities in the large vocabulary continuous speech recognition (LVCSR) systems. One of the main challenges in the German LVCSR is the recognition of the OOV words. For this purpose, data-driven morphemes are used to provide higher lexical coverage. On the other hand, the probability estimates of a sub-lexical LM could be further improved using feature-rich LMs like maximum entropy (MaxEnt) and class-based LMs. In this work, for a sub-lexical level German LVCSR task, we investigate the use of the multiple morpheme level features as classes for building class-based LMs that are estimated using the state-of-the-art MaxEnt approach. Thus, the benefits of both the MaxEnt LMs and the traditional class-based LMs are effectively combined. Furthermore, we experiment the use of Maximum a-posteriori adaptation over the MaxEnt class-based LMs. We show consistent reductions in both the OOV recognition error rate and the word error rate (WER) on a German LVCSR task from the Quaero project, compared to the traditional class-based and the N-gram morpheme based LM. Copyright © 2013 ISCA.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Morpheme level hierarchical pitman-Yor class-based language models for LVCSR of morphologically rich languages

Morpheme level hierarchical pitman-Yor class-based language ...

引用

14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013

作者： El-Desoky Mousa, Amr Shaik, M. Ali Basha Schlüter, Ralf Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

Performing large vocabulary continuous speech recognition (LVCSR) for morphologically rich languages is considered a challenging task. The morphological richness of such languages leads to high out-of-vocabulary (OOV) rates and poor language model (LM) probabilities. In this case, the use of morphemes has been shown to increase the lexical coverage and lower the LM perplexity. Another approach used to improve the LM probability estimates is to incorporate additional knowledge sources in the LM estimation process using classbased LMs (CLMs). Recently, the hierarchical Pitman-Yor LMs (HPYLMs) have shown superiority over the modified Kneser- Ney (MKN) smoothed N-gram LMs in terms of both perplexity (PPL) and word error rate (WER) on word-based LVCSR tasks. In this paper, hierarchical Pitman-Yor class-based LMs (HPYCLMs) are combined with morpheme level language modeling. This enables the application of the proposed models on top of morpheme-based systems. Experiments are conducted on Arabic and German LVCSR tasks. Consistent performance improvements are obtained for all the available corpora compared to the conventional morpheme-based and class-based LMs. Copyright © 2013 ISCA.

关键词： Modeling languages

来源：评论

学校读者我要写书评

暂无评论

RWTHLM - The RWTH Aachen University neural network language modeling toolkit 15

RWTHLM - The RWTH Aachen University neural network language ...

引用

15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken languages, INTERSPEECH 2014

作者： Sundermeyer, Martin Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

We present a novel toolkit that implements the long short-term memory (LSTM) neural network concept for language modeling. The main goal is to provide a software which is easy to use, and which allows fast training of standard recurrent and LSTM neural network language models. The toolkit obtains state-of-the-art performance on the standard Treebank corpus. To reduce the training time, BLAS and related libraries are supported, and it is possible to evaluate multiple word sequences in parallel. In addition, arbitrary word classes can be used to speed up the computation in case of large vocabulary sizes. Finally, the software allows easy integration with SRILM, and it supports direct decoding and rescoring of HTK lattices. The toolkit is available for download under an open source license. Copyright © 2014 ISCA.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Integrating Gaussian mixtures into deep neural networks: Softmax layer with hidden variables 40

Integrating Gaussian mixtures into deep neural networks: Sof...

引用

40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Tuske, Zoltan Tahir, Muhammad Ali Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Spoken Language Processing Group LIMSI CNRS Paris France

ISBN: (纸本)9781467369978

In the hybrid approach, neural network output directly serves as hidden Markov model (HMM) state posterior probability estimates. In contrast to this, in the tandem approach neural network output is used as input features to improve classic Gaussian mixture model (GMM) based emission probability estimates. This paper shows that GMM can be easily integrated into the deep neural network framework. By exploiting its equivalence with the log-linear mixture model (LMM), GMM can be transformed to a large softmax layer followed by a summation pooling layer. Theoretical and experimental results indicate that the jointly trained and optimally chosen GMM and bottleneck tandem features cannot perform worse than a hybrid model. Thus, the question 'hybrid vs. tandem' simplifies to optimizing the output layer of a neural network. Speech recognition experiments are carried out on a broadcast news and conversations task using up to 12 feed-forward hidden layers with sigmoid and rectified linear unit activation functions. The evaluation of the LMM layer shows recognition gains over the classic softmax output. © 2015 IEEE.

关键词： ASR bottleneck DNN GMM hybrid LMM Log-linear mixture model neural network tandem

来源：评论

学校读者我要写书评

暂无评论

Extraction methods of voicing feature for robust speech recognition 8

Extraction methods of voicing feature for robust speech reco...

引用

8th European Conference on Speech Communication and technology, EUROSPEECH 2003

作者： Zolnay, András Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Department of Computer Science VI RWTH Aachen - University of Technology Aachen52056 Germany

In this paper, three different voicing features are studied as additional acoustic features for continuous speech recognition. The harmonic product spectrum based feature is extracted in frequency domain while the autocorrelation and the average magnitude difference based methods work in time domain. The algorithms produce a measure of voicing for each time frame. The voicing measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis to choose the most relevant features. Experiments have been performed on small and large vocabulary tasks. The three different voicing measures combined with MFCCs resulted in similar improvements in word error rate: improvements of up to 14% on the small-vocabulary task and improvements of up to 6% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

Investigation of Transformer-based Latent Attention Models for Neural Machine Translation 14

Investigation of Transformer-based Latent Attention Models f...

引用

14th Conference of the Association for Machine Translation in the Americas, AMTA 2020

作者： Bahar, Parnia Makarov, Nikita Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Current neural translation networks are based on an effective attention mechanism that can be considered as an implicit probabilistic notion of alignment. Such architectures do not guarantee a high quality alignment, even though alignments can easily be used for explainable machine translation. This work describes a latent variable attention model using the transformer architecture, where we carry out an approximate marginalization over alignments. We show that the alignment quality in transformer models can be improved by introducing a latent variable for the alignments. To study the effect of the latent model, we quantitatively and qualitatively analyze the extracted alignments from the multi-head attention. We demonstrate that this method slightly improves translation quality on four WMT 2018 shared translation tasks, as well as generating more focused alignments for better interpretability. © 2020 AMTA 2020 - 14th Conference of the Association for Machine Translation in the Americas, Proceedings. All rights reserved.

关键词： Alignment

来源：评论

学校读者我要写书评

暂无评论

Cipher type detection

Cipher type detection

引用

2014 Conference on Empirical Methods in Natural language Processing, EMNLP 2014

作者： Nuhn, Malte Knight, Kevin Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Information Sciences Institute University of Southern California United States

ISBN: (纸本)9781937284961

Manual analysis and decryption of enciphered documents is a tedious and error prone work. Often-even after spending large amounts of time on a particular cipher-no decipherment can be found. Automating the decryption of various types of ciphers makes it possible to sift through the large number of encrypted messages found in libraries and archives, and to focus human effort only on a small but potentially interesting subset of them. In this work, we train a classifier that is able to predict which encipherment method has been used to generate a given ciphertext. We are able to distinguish 50 different cipher types (specified by the American Cryptogram Association) with an accuracy of 58.5%. This is a 11.2% absolute improvement over the best previously published classifier. © 2014 Association for Computational Linguistics.

关键词： Cryptography

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24页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：