2013 IEEE International Conference on Acoustics, Speech and Signal Processing最新文献_第3页

A practical, self-adaptive voice activity detector for speaker verification with noisy telephone and microphone data 一个实用的，自适应的语音活动检测器扬声器验证与嘈杂的电话和麦克风数据

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6639066

T. Kinnunen, Padmanabhan Rajan

引用次数: 109

Grapheme and multilingual posterior features for under-resourced speech recognition: A study on Scottish Gaelic 资源不足语音识别的字素和多语言后验特征:苏格兰盖尔语的研究

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6639087

Ramya Rasipuram, P. Bell, M. Magimai.-Doss

引用次数: 18

Clustering similar acoustic classes in the Fishervoice framework 在fishvoice框架中聚类相似的声学类

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6639167

Na Li, W. Jiang, H. Meng, Zhifeng Li

引用次数: 0

Identification of genes consistently co-expressed in multiple microarray datasets by a genome-wide Bi-CoPaM approach 通过全基因组Bi-CoPaM方法鉴定在多个微阵列数据集中一致共表达的基因

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6637835

Basel Abu-Jamous, Rui Fa, D. Roberts, A. Nandi

{"title":"Identification of genes consistently co-expressed in multiple microarray datasets by a genome-wide Bi-CoPaM approach","authors":"Basel Abu-Jamous, Rui Fa, D. Roberts, A. Nandi","doi":"10.1109/ICASSP.2013.6637835","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6637835","url":null,"abstract":"Many methods have been proposed to identify informative subsets of genes in microarray studies in order to focus the research. For instance, the recently proposed binarization of consensus partition matrices (Bi-CoPaM) method has, amongst its various features, the ability to generate tight clusters of genes while leaving many genes unassigned from all clusters. We propose exploiting this particular feature by applying the Bi-CoPaM over genome-wide microarray data from multiple datasets to generate more clusters than required. Then, these clusters are tightened so that most of their genes are left unassigned from all clusters, and most of the clusters are left totally empty. The tightened clusters, which are still not empty, include those genes that are consistently co-expressed in multiple datasets when examined by various clustering methods. An example of this is demonstrated in this paper for cyclic and acyclic genes as well as for genes that are highly expressed and that are not. Thus, the results of our proposed approach cannot be reproduced by other methods of genes' periodicity identification or by other methods of clustering.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127850095","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: 6

A comparative study of different classifiers for detecting depression from spontaneous speech 不同分类器检测自发性言语抑郁的比较研究

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6639227

Sharifa Alghowinem, Roland Göcke, M. Wagner, J. Epps, Tom Gedeon, M. Breakspear, G. Parker

{"title":"A comparative study of different classifiers for detecting depression from spontaneous speech","authors":"Sharifa Alghowinem, Roland Göcke, M. Wagner, J. Epps, Tom Gedeon, M. Breakspear, G. Parker","doi":"10.1109/ICASSP.2013.6639227","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6639227","url":null,"abstract":"Accurate detection of depression from spontaneous speech could lead to an objective diagnostic aid to assist clinicians to better diagnose depression. Little thought has been given so far to which classifier performs best for this task. In this study, using a 60-subject real-world clinically validated dataset, we compare three popular classifiers from the affective computing literature - Gaussian Mixture Models (GMM), Support Vector Machines (SVM) and Multilayer Perceptron neural networks (MLP) - as well as the recently proposed Hierarchical Fuzzy Signature (HFS) classifier. Among these, a hybrid classifier using GMM models and SVM gave the best overall classification results. Comparing feature, score, and decision fusion, score fusion performed better for GMM, HFS and MLP, while decision fusion worked best for SVM (both for raw data and GMM models). Feature fusion performed worse than other fusion methods in this study. We found that loudness, root mean square, and intensity were the voice features that performed best to detect depression in this dataset.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126193558","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: 82

Mitigation of clipping in sensors 减轻传感器的削波

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638803

Shang-Kee Ting, A. H. Sayed

引用次数: 7

Adaptive feature split selection for co-training: Application to tire irregular wear classification 自适应特征分割选择协同训练:在轮胎不规则磨损分类中的应用

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638308

Wei Du, R. Phlypo, T. Adalı

引用次数: 4

Interactive multimodal music transcription 交互式多模态音乐转录

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6637639

J. Quereda, C. Pérez-Sancho

引用次数: 9

Discriminatively trained Bayesian speaker comparison of i-vectors 判别训练贝叶斯说话人比较的i向量

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6639153

B. J. Borgstrom, A. McCree

引用次数: 20

Tracking sparse signal sequences from nonlinear/non-Gaussian measurements and applications in illumination-motion tracking 从非线性/非高斯测量中跟踪稀疏信号序列及其在光照运动跟踪中的应用

2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638941

Rituparna Sarkar, Samarjit Das, Namrata Vaswani

引用次数: 6