2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)最新文献_第4页

A syllable-based Turkish speech recognition system by using time delay neural networks (TDNNs) 基于音节的延时神经网络(TDNNs)土耳其语语音识别系统

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054130

Burcu Can, Harun Artuner

{"title":"A syllable-based Turkish speech recognition system by using time delay neural networks (TDNNs)","authors":"Burcu Can, Harun Artuner","doi":"10.1109/SOCPAR.2013.7054130","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054130","url":null,"abstract":"In this paper, we present a model for Turkish speech recognition. The model is syllable-based, where the recognition is performed through syllables as speech recognition units. The main goal of the model is to recognize as much as possible of a given continuous speech by identifying only a small set of syllables in the language. For that purpose, only the syllable types with a higher frequency are selected for the recognition. The use of longer recognition units in speech recognition systems increases the success of the recognition since it is easier to detect the endpoints of syllables when compared to phonemes. On the other side, word-based recognition requires a very large dataset that includes all the words and word forms in the language, which is also another challenge. Hereby, we take the advantage of Turkish being an ortographically transparent and syllabified language. Our model employs time delay neural networks (TDNNs) for learning syllables. We achieve an accuracy of %65.6 on our large vocabulary continuous speech corpus. In addition, we define an algorithm for the automatic detection of syllable boundaries which gives an accuracy of %44. The automatic syllable boundary detection module is used for the recognition of isolated syllables rather than a continuous speech.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134016367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Differential evolution with nonlinear simplex method and dynamic neighborhood search 基于非线性单纯形法和动态邻域搜索的差分进化

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054154

Dang Cong Tran, Zhijian Wu, Hui Wang, V. H. Tran

引用次数: 0

Remarks on model reference self-tuning PID controller using quantum neural network with qubit neurons 使用带有量子位神经元的量子神经网络的模型参考自整定 PID 控制器评述

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054138

Kazuhiko Takahashi, Y. Shiotani, M. Hashimoto

引用次数: 3

Categorical term frequency probability based feature selection for document categorization 基于分类词频率概率的文档分类特征选择

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054103

Qiang Li, Liang He, Xin Lin

{"title":"Categorical term frequency probability based feature selection for document categorization","authors":"Qiang Li, Liang He, Xin Lin","doi":"10.1109/SOCPAR.2013.7054103","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054103","url":null,"abstract":"Document categorization technology heavily relies on the categorical distribution of features. Those terms which occur unevenly in various categories have strong distinguishable information as to categorization. At first, we give the definition of CTFP (Categorical Term Frequency Probability), which will be used to accurately reflect the categorical characteristics of terms on each category. Then, the CTFP_VM (Variance-Mean based on CTFP) feature selection criterion is introduced to reveal the category distribution difference. After computing and ranking the variance mean based on CTFP distribution for each term, feature sets are obtained for document categorization. We perform the document categorization experiments on SVM classifiers with the well-known Reuters-21578 and 20 news-18828 corpuses as unbalanced and balanced corpus respectively. Experiments compare the novel methods with other conventional feature selection algorithms and the proposed method achieves the best feature set for document categorization The experimental results also demonstrate that the proposed variance mean feature selection method base on CTFP not only has better Fl-metric for document categorization but excellent corpus adaptability.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134502384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

What should I comment: Recommending posts for commenting 我应该评论什么:推荐评论帖子

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054112

N. Hai, Ngo Xuan Bach, Tran Quang An, Tu Minh Phuong

引用次数: 0

Time-series forecasting using Bagging techniques and reservoir computing 利用Bagging技术和油藏计算进行时序预测

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054117

Sebastián Basterrech, V. Snás̃el

{"title":"Time-series forecasting using Bagging techniques and reservoir computing","authors":"Sebastián Basterrech, V. Snás̃el","doi":"10.1109/SOCPAR.2013.7054117","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054117","url":null,"abstract":"In this paper we present a general procedure to use Bagging techniques for time series processing and forecasting problems Bagging is one of the most used techniques for combining several predictors in order to produce a highly accurate method. The method uses bootstrap replications of the original training set and for each replicate sample one predictor is generated. After that the method combines the predictors using the majority vote for classification problems and the average function for regression problems In temporal learning tasks, the order serial of the data precludes to realize bootstrap samples Here, we present an approach which uses a recurrent neural network to transform the spatio-temporal information of the input data in a new larger space In this new space is possible to apply bootstrap techniques. In this initial paper, we evaluate our approach on 4 time series benchmarks using linear regressions Although, the idea presented here is more general and can be used with other kind of statistical methods such that CART, SVM, and so on. The empirical results show the power of this new approach to achieve good performances in temporal learning tasks.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121148807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A classifier based approach to real-time fall detection using low-cost wearable sensors 基于分类器的低成本可穿戴传感器实时跌倒检测方法

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1145/2676585.2676597

Nguyen Ngoc Diep, Cuong Pham, Tu Minh Phuong

引用次数: 17

Identifying coordinated compound words for Vietnamese word segmentation 越南语分词中协调复合词的识别

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054145

T. Anh, Thanh Tinh Dao, Phuong-Thai Nguyen

引用次数: 1

Interpolative reasoning approach to sparse general type-2 fuzzy rules based on the reduced grid representation 基于简化网格表示的稀疏一般2型模糊规则插值推理方法

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054104

L. Ngo, M. Vu, K. Hirota

引用次数: 1

Edge direction matrixes-based local binary patterns descriptor for invariant pattern recognition 基于边缘方向矩阵的局部二值模式描述符的不变模式识别

2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR) Pub Date : 2013-12-01 DOI: 10.1109/SOCPAR.2013.7054123

M. A. Talab, S. Abdullah, M. Razalan

{"title":"Edge direction matrixes-based local binary patterns descriptor for invariant pattern recognition","authors":"M. A. Talab, S. Abdullah, M. Razalan","doi":"10.1109/SOCPAR.2013.7054123","DOIUrl":"https://doi.org/10.1109/SOCPAR.2013.7054123","url":null,"abstract":"Invariant descriptor for shape and texture image recognition usage is an essential branch of pattern recognition. It is made up of techniques that aim at extracting information from shape images via human knowledge and works. The descriptors need to have strong Local Binary Pattern (LBP) in order to encode the information distinguishing them. Local Binary Pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. It is needed as the edge direction matrices (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main objective of this paper is the need of improved recognition capabilities which achieved by the combining LBP and EDMS. Working together, these two descriptors will add advantages to the program and enable the researcher to investigate the weaknesses of each one. Two classifiers are used: multi-layer neural network and random forest. The techniques used in this paper are compared with Gray-Level Co-occurrence matrices (GLCM-EDMS) and Scale Invariant Feature Transform (SIFT) by using two benchmark dataset: MPEG-7 CE-Shape-1 for shape and Arabic calligraphy for texture. The experiments have shown the superiority of the introduced descriptor over the GLCM-EDMS and the SIFT.","PeriodicalId":315126,"journal":{"name":"2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131308133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5