This work presents an acoustic model adaptation method for speaker verification (SV) in environments with additive noise. In contrast to traditional acoustic model adaptation techniques that adapt the models parameter...
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This work presents an acoustic model adaptation method for speaker verification (SV) in environments with additive noise. In contrast to traditional acoustic model adaptation techniques that adapt the models parameters based on a model of the noise, acoustic model enhancement (AME) belongs to a new scheme in which the models are adapted to the speech enhancement strategy. The theoretical framework is presented for spectral subtraction (SS) as the enhancement technique and GMM as the acoustic models. In order to study the effect of additive noise only, a modified TIMIT dataset was used. The experimental setup uses two types of noise: one with fixed spectrum that helps as a proof of concept, and another with time-varying spectrum as a more realistic performance reference for AME. The results for this latter type show that at 20 dB SNR, the equal error rate (EER) dropped from 17% to around 8.9% when the noisy speech was enhanced with SS, whereas it further dropped to 8.1% with AME.
This paper describes a real-time multi-camera surveillance system that can be applied to a range of application domains. This integrated system is designed to observe crowded scenes and has mechanisms to improve track...
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Automatic keyword extraction is one of the most important techniques in natural language processing. In this paper, features of complex networks composed of Chinese are studied. A novel automatic keyword extraction al...
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Automatic keyword extraction is one of the most important techniques in natural language processing. In this paper, features of complex networks composed of Chinese are studied. A novel automatic keyword extraction algorithm for Chinese document is proposed which is based on the features of the complex networks according to the small world structure in language networks and the theoretical achievements in complex networks. It extracts keyword based on the feature values of the word nodes in a documental language network. Experimental results show the proposed algorithm obtains higher average precision compared with the keyword extraction algorithm based on TFIDF.
In this paper we examine the issue of optimizing disk usage and scheduling large-scale scientific workflows onto distributed resources where the workflows are data-intensive, requiring large amounts of data storage, a...
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4 RESULTS Results of factor experiments 23,i.e.2 levels for 3 experimental factors(temperature in 1st stage of hydrolysis,pH of reaction mixture in 3rd stage of hydrolysis and duration of 3rd stage of hydrolysis) are ...
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4 RESULTS Results of factor experiments 23,i.e.2 levels for 3 experimental factors(temperature in 1st stage of hydrolysis,pH of reaction mixture in 3rd stage of hydrolysis and duration of 3rd stage of hydrolysis) are presented in *** analysis of hydrolysate or of remaining chrome cake it is obvious that yield of nitrogen in hydrolysate,in dependence on fluctuating reaction conditions,ranged in quite narrow limits 58%-65%.
作者：
纪建田铮Department of Computer Science & Technology
Northwestern Polytechnical University Xi'an 710072 Department of Applied Mathematics
Northwestern Polytechnical UniversityXi'an 710072 Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhan 430074
The separation of noisy image is a very exciting area of research, especially when no prior information is available about the noisy image. In this paper, we propose a robust independent component analysis （ICA） net...
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The separation of noisy image is a very exciting area of research, especially when no prior information is available about the noisy image. In this paper, we propose a robust independent component analysis （ICA） network for separation images contaminated with high-level additive noise or outliers. We reduce the power of additive noise by adding outlier rejection rule in ICA. Extensive computer simulations confirm robustness and the excellent performance of the resulting algorithms.
A framework for dialectal Chinese speech recognition is proposed and studied, in which a relatively small dialectal Chinese （or in other words Chinese influenced by the native dialect） speech corpus and dialect-rela...
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A framework for dialectal Chinese speech recognition is proposed and studied, in which a relatively small dialectal Chinese （or in other words Chinese influenced by the native dialect） speech corpus and dialect-related knowledge are adopted to transform a standard Chinese （or Putonghua, abbreviated as PTH） speech recognizer into a dialectal Chinese speech recognizer. Two kinds of knowledge sources are explored： one is expert knowledge and the other is a small dialectal Chinese corpus. These knowledge sources provide information at four levels： phonetic level, lexicon level, language level, and acoustic decoder level. This paper takes Wu dialectal Chinese （WDC） as an example target language. The goal is to establish a WDC speech recognizer from an existing PTH speech recognizer based on the Initial-Final structure of the Chinese language and a study of how dialectal Chinese speakers speak Putonghua. The authors propose to use contextindependent PTH-IF mappings （where IF means either a Chinese Initial or a Chinese Final）, context-independent WDC-IF mappings, and syllable-dependent WDC-IF mappings （obtained from either experts or data）, and combine them with the supervised maximum likelihood linear regression （MLLR） acoustic model adaptation method. To reduce the size of the multipronunciation lexicon introduced by the IF mappings, which might also enlarge the lexicon confusion and hence lead to the performance degradation, a Multi-Pronunciation Expansion （MPE） method based on the accumulated uni-gram probability （AUP） is proposed. In addition, some commonly used WDC words are selected and added to the lexicon. Compared with the original PTH speech recognizer, the resulting WDC speech recognizer achieves 10-18% absolute Character Error Rate （CER） reduction when recognizing WDC, with only a 0.62% CER increase when recognizing PTH. The proposed framework and methods are expected to work not only for Wu dialectal Chinese but also for other dialectal Chinese languages and
作者：
Prof. Jian-Xin XuProf. Leonid FridmanDepartment of Electrical and Computer Eng. National University of Singapore 4 Engineering Drive 3 Singapore 117576 Tel +65 6874-2566
Fax +65 6779-1103 Dr Jian-Xin Xu received his Bachelor degree from Zhejiang University
China in 1982. He attended the University of Tokyo Japan where he received his Master's and Ph.D. degrees in 1986 and 1989 respectively. All his degrees are in Electrical Engineering. He worked for one year in the Hitachi research Laboratory Japan and for more than one year in Ohio State University U.S.A. as a Visiting Scholar. In 1991 he joined the National University of Singapore and is currently an associate professor in the Department of Electrical Engineering. His research interests lie in the fields of learning control variable structure control fuzzy logic control discontinuous signal processing and applications to motion control and process control problems. He is the associate editor of Asian Journal of Control member of TC on variable structure systems and sliding mode control of IEEE Control Systems Society and a senior member of IEEE. He has produced more than 90 peer-refereed journal papers near 160 technical papers in conference proceedings and authored/edited 4 books. Division de Estudios de Posgrado Facultad de Ingenieria National Autonomous University of Mexico DEP-FI
UNAM Edificio “A” Circuito Exterior Ciudad Universitaria A. P. 70–256 C.P.04510 Mexico D.F. Mexico Tel +52 55 56223014 Fax +52 55 56161719 Dr. Leonid M. Fridman received his M.S in mathematics from Kuibyshev (Samara) State University
Russia Ph.D. in Applied Mathematics from Institute of Control Science (Moscow) and Dr. of Science degrees in Control Science from Moscow State University of Mathematics and Electronics in 1976 1988 and 1998 respectively. In 1976–1999 Dr. Fridman was with the Department of Mathematics at the Samara State Architecture and Civil Engineering Academy Samara Russia. In 2000–2002 he was with the Department of Postgraduate Study and Investigations at the Chihuahu
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