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International Conference Oriental COCOSDA

作者： Tulika Basu Arup Saha Advanced Speech Processing Group Center for Development of Advanced Computing Kolkata Kolkata India

ISBN: (纸本)9781479936755

In speech synthesis the role of prosody is very crucial. To make the synthesized speech more natural and soothing to the human ears various prosody and intonation model together with emotional model have been experimented over last few decades. Apart from the segmental quality and voice characteristics, it depends mostly on the quality of the prosody model which is responsible for the naturalness of any TTS system. But as it is very hard to evaluate prosody model in an objective way, a perceptual comparison method is adopted in this work to evaluate prosody model.

关键词： Index Terms- Prosody intonation evaluation perception.

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Studying the role of data quality on statistical and neural machine translation 10

Studying the role of data quality on statistical and neural ...

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10th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2021

作者： Arora, Karunesh Kumar Tomar, Geetam S. Agrawal, Shyam S. Speech and Natural Language Processing Centre for Development of Advanced Computing Sector 62 Noida India Rajkiya Engineering College UP Sonbhadra India KIIT Group of Institutions Sohna Road Bhondsi Gurugram India

ISBN: (纸本)9780738105239

Statistical and Neural Machine translation techniques are based on the parallel data used for training models. The general belief is that more training data would result in better models. We studied the available corpora and ambient noises present in them. It revealed that the available data is highly noisy. The paper describes various types of noises present in there and how these are identified. Different types of noise filters are developed and normalization processes have been applied on the corpora. Statistical and neural machine translation models are trained to study the impact of cleaning of noisy data. We performed experiments with noisy data and with cleaned data after discarding noisy data from the training corpus. Standard test set WMT-14 has been used for performing evaluation. The quality of machine translation has been measured through BLEU scores. It was observed that even after discarding a significant volume of noisy data, the models without noisy data performed better than the corpus containing noises. It proves that quality of data has significant impact and mere having huge piles of uncleaned data in not a good choice. The test case presented here is for English-Hindi language pair. It also shows a path that for low resource language pairs, paying attention to the quality of data would bring returns in form of better translation performance. As the noises discussed in paper are general in nature, the findings should be true for any other Indian language pair also, due to inherent similarity among Indian languages. © 2021 IEEE.

关键词： Neural machine translation

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speech Enhancement via Deep Spectrum Image Translation Network 26

Speech Enhancement via Deep Spectrum Image Translation Netwo...

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26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019

作者： Kashani, Hamidreza Baradaran Jodeiri, Ata Goodarzi, Mohammad Mohsen Rezaei, Iman Sarraf Amirkabir University of Technology Electrical Engineering Faculty Tehran Iran College of Engineering University of Tehran School of Electrical Computer Engineering Tehran Iran Buein Zahra Technical University Department of Electrical and Computer Engineering Qazvin Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (纸本)9781728156637

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptuallymodified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement methods in terms of both quality and intelligibility measures, represented by PESQ and ESTOI, respectively. © 2019 IEEE.

关键词： Audition

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Simultaneous gene selection and cancer classification using chemical reaction optimization

Simultaneous gene selection and cancer classification using ...

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World Congress on Engineering, WCE 2014

作者： Doshi, Jitesh Chindhe, Mahesh Kharche, Yogesh Ghosh, Shameek Valadi, Jayaraman Bionfonrmatics Centre University of Pune Pune India Bioengineering Dept. University of Illinois at Chicago Chicago IL 60607 United States Dept. of Computer Science University of Pune Pune Maharashtra 411007 India Evolutionary Computing and Image Processing Group Centre for Development of Advanced Computing University of Pune Pune Maharashtra 411007 India Advanced Analytics Institute University of Technology Sydney Broadway NSW 2007 Australia Center for Informatics Shiv Nadar University Chithera Dadri Gautam Budh Nagar Uttar Pradesh 203207 India

ISBN: (纸本)9789881925275

Microarray gene expression datasets comprise of a large number of genes in contrast to a small number of samples, thus having a high dimension of variables. Analysis of microarray data can lead us to many useful conclusions. In many microarray data analyses, selecting a small subset of genes which are of significance for a particular type of disease is an important issue but selection of such genes become difficult due to many irrelevant genes and noisy genes. The process of gene selection helps to extract the most informative genes, which consequently aid to build a robust prediction model using those genes. In this study, we employ a hybrid Chemical Reaction Optimization (CRO) based filter-wrapper methodology, which uses an information gain gene ranking heuristic to simultaneously extract informative gene subsets and build robust cancer classification models. The performance of the proposed method was tested on three benchmark gene expression datasets obtained from the Kent Ridge Biomedical datasets collection and the LIBSVM data repository. CRO results demonstrate its capability to select relevant genes with high confidence in comparison to the results reported earlier.

关键词： Classification (of information)

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On Metric-based Deep Embedding Learning for Text-Independent Speaker Verification

On Metric-based Deep Embedding Learning for Text-Independent...

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International Conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Hamidreza Baradaran Kashani Shaghayegh Reza Iman Sarraf Rezaei Computer Engineering Faculty Isfahan University Isfahan Iran Biomedical Engineering Faculty Amirkabir University of Technology Tehran Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

ISBN: (数字)9781728186290

ISBN: (纸本)9781728186306

As a state-of-the-art solution for speaker verification problems, deep neural networks have been usefully employed for extracting speaker embeddings which represent speaker informative features. Objective functions, as the supervisors for the learning of discriminative embeddings, play a crucial role for this purpose. In this paper, motivated by the success of metric learning approaches, we investigate four newly proposed metrics in the literature, specifically for the speaker verification problem. For deeper comparisons, we consider these metrics from both main groups of metric-based objectives, i.e. instance-based and proxy-based ones. By considering embeddings as instances, the first group exploits the instance-to-instance relations, while the latter associates the instances to the proxies as representatives of training *** in terms of Equal Error Rate (EER) are conducted in two conventional manners: end-to-end and modular where cosine similarity and PLDA are applied to embeddings, respectively. Experimental results show that in the case of end-to-end, instances-based metrics outperform proxy-based ones, while interestingly the opposite behavior is gained for the modular case. Finally, the lowest EER is achieved by adopting one of the proxy-based metrics, namely SoftTriple, in the modular manner. It yields relative improvements up to 12% compared to the state-of-the art method, i.e. x-vector.

关键词： Measurement Training Neural networks Signal processing Feature extraction Linear programming Intelligent systems

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speech enhancement via deep spectrum image translation network

arXiv

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arXiv 2019年

作者： Kashani, Hamidreza Baradaran Goodarzi, Mohammad Mohsen Jodeiri, Ata Rezaei, Iman Sarraf Electrical Engineering Faculty Amirkabir University of Technology Tehran Iran Department of Electrical and Computer Engineering Buein Zahra Technical University Qazvin Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Speech and Language Processing Group Research Center for Development of Advanced Technologies Tehran Iran

Quality and intelligibility of speech signals are degraded under additive background noise which is a critical problem for hearing aid and cochlear implant users. Motivated to address this problem, we propose a novel speech enhancement approach using a deep spectrum image translation network. To this end, we suggest a new architecture, called VGG19-UNet, where a deep fully convolutional network known as VGG19 is embedded at the encoder part of an image-to-image translation network, i.e. U-Net. Moreover, we propose a perceptually-modified version of the spectrum image that is represented in Mel frequency and power-law non-linearity amplitude domains, representing good approximations of human auditory perception model. By conducting experiments on a real challenge in speech enhancement, i.e. unseen noise environments, we show that the proposed approach outperforms other enhancement methods in terms of both quality and intelligibility measures, represented by PESQ and ESTOI, respectively. Copyright © 2019, The Authors. All rights reserved.

关键词： Audition

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speech Enhancement via Deep Spectrum Image Translation Network

Speech Enhancement via Deep Spectrum Image Translation Netwo...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Hamidreza Baradaran Kashani Ata Jodeiri Mohammad Mohsen Goodarzi Iman Sarraf Rezaei Electrical Engineering Faculty Amirkabir University of Technology Tehran Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering Buein Zahra Technical University Qazvin Iran Research Center for Development of Advanced Technologies Speech and Language Processing Group Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156644

关键词： speech enhancement Noise measurement Feature extraction Training Computer architecture Convolution Frequency-domain analysis

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Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

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BMC bioinformatics 2016年第Sup3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Bart Cuypers Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca NY USA Graduate Field of Computational Biology Cornell University Ithaca NY USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Nationalestraat 155 2000 Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Middelheimlaan 1 2020 Antwerpen Belgium Department of Computer Science University of Arizona Tucson AZ 84721 USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium iMinds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai 600036 Tamilnadu India Philips Research North America 345 Scarborough Road Briarcliff Manor NY 10510 USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8550 Japan Systems Biology C

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A Systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

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