توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱A New Evolutionary Algorithm for Structure Learning in Bayesian Networks
نویسنده(ها): ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
A new structure learning approach for Bayesian networks (BNs) based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be essentially considered as an evolutionary based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem; this leads to the fitter individual. The proposed method is applied to real–world and benchmark applications, while its effectiveness is demonstrated through computer simulation. Results of simulation show that ARO outperforms GA because ARO results good structure in comparison with GA and the speed of convergence in ARO is more than GA. Finally, the ARO performance is statistically shown<\div>

۲Improvement of Language Identification Performance Using Generalized Phone Recognizer
نویسنده(ها): ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۵
Two popular and better performing approaches to language Identification (LID) are Phone Recognition followed by Language Modeling (PRLM) and Parallel PRLM. In this paper, we report several improvements in Phone Recognition which reduces error rate in PRLM and PPRLM based LID systems. In our previous paper, we introduced APRLM approach that reduceserror rate for about 1.3% in LID tasks. In this paper, we suggest other solution that overcomes APRLM. This new LID approach is named Generalized PRLM or GPRLM. Several language identification experiments were conducted and the proposed improvements were evaluated using OGI–MLTS corpus. Our results show that GPRLM overcomes PPRLM and APRLM about 2.5% and 1.2% respectively in two language classification tasks.<\div>

۳Speaker Clustering Performance Improvement using Eigen–Voice Speaker Adaptation
نویسنده(ها): ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
One of the most important phases of speaker indexing is speaker clustering which aims to find the number of speakers in a speech document and merge the speech segments corresponding to a single speaker. The most critical source of problem in speaker clustering is the speech segments duration which may be so short that proper segment modeling becomes hard to achieve. An alternative suggestion in these situations is to adapt global models with new data instead of building the speaker models from the ground. In this paper we investigate two adaptation techniques in eigen–voice space for improving clustering performance especially for shorter speech utterances. These techniques were embedded in a clustering framework and evaluated on a set of domestic conversational speech. We have also compared the proposed methods with some other known techniques. The experiments show a considerable improvement in speaker clustering performance.<\div>

۴Speaker Identification in Noisy Environments Using Dynamic Bayesian Networks
نویسنده(ها): ، ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
This paper describes the theory and implementation of dynamic Bayesian networks in the context of speaker identification. Dynamic Bayesian networks provide a succinct and expressive graphical language for factoring joint probability distributions, and we begin by presenting the structures that are appropriate for doing speaker identification in clean and noisy environments. This approach is notable because it expresses an identification system using only the concepts of random variables and conditional probabilities. We present illustrative experiments in both clean and noisy environments and our experiments show that this new approach is very promising in the field of speaker identification.<\div>

۵Support Vector Machines for Speaker Based Speech Indexing
نویسنده(ها): ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
This paper proposes an integrated framework for speaker indexing which includes both speaker segmentation and speaker clustering. Speaker indexing systems has wide domains of application with different requirements which make a general speaker indexing framework hard to accomplish. The main source of performance degradation in speaker indexing is the probable existence of short speech utterances which makes the speaker turns hard to distinguish and also exposes the segment modeling to data insufficiency. This paper introduces a speaker indexing framework with high average performance which uses Support Vector Machines (SVM) as the core approach. The main contribution of this framework is the SVM based clustering approach which makes the indexing more robust against the short speech segments. This framework is evaluated on a domestic conversational speech dataset and the results were satisfactory.<\div>
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