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Research on Speech Quality Evaluation Method Based on Deep Neural Network 19

Research on Speech Quality Evaluation Method Based on Deep N...

引用

proceedings of the 2019 2nd international conference on E-Business, Information Management and Computer Science

作者： Teng Haikun Wang Shiying Li Lunbin Computer and Information Engineering College Heihe University Heihe China

ISBN: (纸本)9781450366496

With the continuous development of artificial intelligence research and high-performance computers, deep learning and related theories, neural networks have become mainstream acoustic models. This article starts with the overall framework and basic principles of speech recognition, and studies the language recognition system under deep neural network. Through deep neural network, it can better deal with the large amount of Chinese data processing process, reduce model complexity, and improve the accuracy of Chinese speech recognition rate. For this reason, this paper uses the deep neural network-based Chinese speech recognition system as the research object to study the speech quality after recognition. The results verify that the deep neural network language model has a better recognition accuracy.

关键词： Deep learning

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Minimum Classification Error Training with Speech Synthesis-Based Regularization for Speech Recognition 19

Minimum Classification Error Training with Speech Synthesis-...

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proceedings of the 2019 2nd international conference on signal processing and machine learning

作者： Naoto Umezaki Takumi Okubo Hideyuki Watanabe Shigeru Katagiri Miho Ohsaki Graduate School of Science and Engineering Doshisha University Kyoto Japan Advanced Telecommunications Research Institute International Kyoto Japan

ISBN: (纸本)9781450372213

To increase the utility of Regularization, which is a common framework for avoiding the underestimation of ideal Bayes error, for speech recognizer training, we propose a new classifier training concept that incorporates a regularization term that represents the speech synthesis ability of classifier parameters. To implement our new concept, we first introduce a speech recognizer that embeds Line Spectral Pairs-Conjugate Structure-Algebraic Code Excited Linear Prediction (LSP-CS-ACELP) in a Multi-Prototype State-Transition-Model (MP-STM) classifier, define a regularization term that represents the speech synthesis ability by the distance between a training sample and its nearest MP-STM word model, and formalize a new Minimum Classification Error (MCE) training method for jointly minimizing a conventional smooth classification error count loss and the newly defined regularization term. We evaluated the proposed training method in an isolated-word, closed-vocabulary, and speaker-independent speech recognition task whose Bayes error is estimated to be about 20% and found that our method successfully produced an estimate of Bayes error (about 18.4%) with a single training run over a training dataset without such data resampling as Cross-Validation or the assumptions of sample distribution. Moreover, we investigated the quality of the synthesized speech using LSP parameters derived from the trained prototypes and found that the quality of the Bayes error estimation is clearly supported by the speech synthesis ability preserved in the training.

关键词： Speech recognition Minimum classification error training Line spectral pairs Code-excited linear prediction

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StackDroid: Evaluation of a Multi-level Approach for Detecting the Malware on Android Using Stacked Generalization 2nd

StackDroid: Evaluation of a Multi-level Approach for Detecti...

引用

2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Motiur Rahman, Sheikh Shah Mohammad Saha, Sanjit Kumar Department of Computer Science and Engineering Jahangirnagar University Savar Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9789811391804

Attackers or cyber criminals are getting encouraged to develop android malware because of the rapidly growing rate of android users. To detect android malware, researchers and security specialist have been started to contribute on android malware analysis and detection related tasks using machine learning algorithms. In this paper, Stacked Generalization has been used to minimize the error rate and a multi-level architecture based approach named StackDroid has been presented and evaluated. In this experiment, Extremely Randomized Tree (ET), Random Forest (RF), Multi-Layer Perceptron (MLP) and Stochastic Gradient Descent (SGD) classifiers have been used as base classifiers in level 1 and Extreme Gradient Boosting (EGB) has been used as final predictor in level 2. It’s been found that StackDroid provides 99% of Area Under Curve (AUC), 1.67% of False Positive Rate (FPR) and 97% detection accuracy on DREBIN dataset which provides a strong basement to the development of android malware scanner. © 2019, Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Smart Approach to Build A Deep Neural Network Based IDS for Cloud Environment Using an Optimized Genetic Algorithm 19

Smart Approach to Build A Deep Neural Network Based IDS for ...

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2nd international conference on Networking, Information Systems and Security (NISS)

作者： Chiba, Zouhair Abghour, Noreddine Moussaid, Khalid El Omri, Amina Rida, Mohamed Hassan II Univ Casablanca Fac Sci LIMSAD Labs Casablanca 20100 Morocco

ISBN: (纸本)9781450366458

In recent years cloud computing has revolutionized the IT world with rapidly emerging and widely accepted paradigm for computing systems, and it has become popular among users in organizations and companies. Nowadays, numerous organizations have begun to upload their massive amount of prominent data into public cloud. Nevertheless, uploading sensitive data to open and distributed public cloud storage services poses security risks such as availability, confidentiality and integrity to organizations. Moreover, non-stop cloud services have caused high levels of intrusion and abuse. Thereby, protecting network accessible Cloud resources and services from various threats and attacks is of great concern. To address this issue, it is imperative to develop a powerful Network Intrusion System (NIDS) to detect both outsider and insider intruders with high detection precision in the cloud environment. In this work, we propose a smart approach using an Improved Genetic Algorithm (IGA) to build a Deep Neural Network (DNN) based anomaly NIDS. Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Our approach consists to use IGA in order to search the optimal or near optimal combination of most relevant values of the parameters included in construction of DNN based IDS or impacting its performance, like feature selection, data normalization, architecture of DNN, activation function, learning rate and Momentum term, which ensure high detection rate, high accuracy and low false alarm rate. CloudSim 4.0 simulator platform and CICIDS 2017 benchmark dataset were used for simulation and validation of the proposed system. The experimental results obtained demonstrate that in comparison to several traditional and recent approaches, our proposed IDS achieves higher detection rate and lower false positive rate.

关键词： Cloud computing Network intrusion detection system Anomaly detection Deep neural network Back propagation learning algorithm Optimization Genetic algorithm CICIDS2017 dataset

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An Automated Online Spam Detector Based on Deep Cascade Forest 2nd

An Automated Online Spam Detector Based on Deep Cascade Fore...

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2nd Annual international conference on Science of Cyber Security (SciSec)

作者： Chen, Kangyang Zou, Xinyi Chen, Xingguo Wang, Huihui Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Peoples R China Beijing Technol & Business Univ Natl Engn Lab Agri Prod Qual Traceabil Beijing Peoples R China Minist Educ Key Lab Intelligent Percept & Syst High Dimens In PCA Lab Nanjing Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Socia Nanjing Peoples R China

ISBN: (纸本)9783030346362;9783030346379

With the development of internet communication, spam is quite ubiquitous in our daily life. It not only disturbs users, but also cms. Although there exists many methods of spam detection in both the area of cyber security and natural language processing, their performance is still not capable to satisfy requirements. In this paper, we implemented deep cascade forest for spam detection, a deep model without using back-propagation. With less hyperparameters, the training cost can be easily controlled and declines compared with that in neutral network methods. Furthermore, the proposed deep cascade forest outperforms other machine learning models in the F1 Score of detection. Therefore, considering the lower training cost, it can be considered as a useful online tool for spam detection.

关键词： Spam detection Deep forest Deep learning machine learning Ensemble methods

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Minimum classification error training with speech synthesis-based regularization for speech recognition 2

Minimum classification error training with speech synthesis-...

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2nd international conference on signal processing and machine learning, SPML 2019

作者： Umezaki, Naoto Okubo, Takumi Watanabe, Hideyuki Katagiri, Shigeru Ohsaki, Miho Graduate School of Science and Engineering Doshisha University Kyoto Japan Advanced Telecommunications Research Institute International Kyoto Japan

ISBN: (纸本)9781450372213

关键词： Speech recognition

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Classifying Arabic Farmers’ Complaints Based on Crops and Diseases Using machine learning Approaches 2nd

Classifying Arabic Farmers’ Complaints Based on Crops and D...

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2nd international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2018

作者： Ali, Mostafa Guru, D.S. Suhil, Mahamad Department of Studies in Computer Science University of Mysore Mysore India

ISBN: (纸本)9789811391866

In this paper, two models are proposed to automatically categorize the farmers’ complaints on various diseases that may affect crops. In the first model, a complaint which is expressed in Arabic free text is classified into its respective crop and then into a particular disease, but in the second model, the complaint is classified directly into diseases. A dataset of farmers’ complaints described in Arabic script consisting of complaints from five different crops raised by farmers of different parts of Egypt is created. Separate lexicons are created for crops and their diseases by considering all possible technical terms related to a crop and its diseases including their possible slang synonyms. Each preprocessed complaint is represented in the form of a binary vector using the vector space model (VSM) with the help of crop lexicon so that machine learning techniques can be applied. Experiments are conducted on the dataset by varying the percentage of training with multiple trials using SVM and KNN classifiers. It has been observed from the results that the proposed model is performing on par with the human expert and can be deployable for real-time operations. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Crops

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Improving rare disease classification using imperfect knowledge graph 7

Improving rare disease classification using imperfect knowle...

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2nd international Workshop on Health Natural Language processing (HealthNLP)

作者： Li, Xuedong Wang, Yue Wang, Dongwu Yuan, Walter Peng, Dezhong Mei, Qiaozhu Sichuan Univ Coll Comp Sci Chengdu Peoples R China Univ North Carolina Chapel Hill Sch Informat & Lib Sci Chapel Hill NC USA MobLab Inc Pasadena CA USA Univ Michigan Sch Informat Ann Arbor MI 48109 USA

ISBN: (纸本)9781538691397;9781538691380

Background: Accurately recognizing rare diseases based on symptom description is an important task in patient triage, early risk stratification, and target therapies. However, due to the very nature of rare diseases, the lack of historical data poses a great challenge to machine learning-based approaches. On the other hand, medical knowledge in automatically constructed knowledge graphs (KGs) has the potential to compensate the lack of labeled training examples. This work aims to develop a rare disease classification algorithm that makes effective use of a knowledge graph, even when the graph is imperfect. Method: We develop a text classification algorithm that represents a document as a combination of a "bag of words" and a "bag of knowledge terms," where a "knowledge term" is a term shared between the document and the subgraph of KG relevant to the disease classification task. We use two Chinese disease diagnosis corpora to evaluate the algorithm. The first one, HaoDaiFu, contains 51,374 chief complaints categorized into 805 diseases. The second data set, ChinaRe, contains 86,663 patient descriptions categorized into 44 disease categories. Results: On the two evaluation data sets, the proposed algorithm delivers robust performance and outperforms a wide range of baselines, including resampling, deep learning, and feature selection approaches. Both classification-based metric (macro-averaged F-1 score) and ranking-based metric (mean reciprocal rank) are used in evaluation. Conclusion: Medical knowledge in large-scale knowledge graphs can be effectively leveraged to improve rare diseases classification models, even when the knowledge graph is incomplete.

关键词： Rare disease diagnosis Knowledge graph machine learning Text classification Extremely imbalanced data

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2nd international EAI conference on Emerging Technologies for Developing Countries, AFRICATEK 2018

2nd International EAI Conference on Emerging Technologies fo...

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2nd international EAI conference on Emerging Technologies for Developing Countries, AFRICATEK 2018

ISBN: (纸本)9783030051976

The proceedings contain 16 papers. The special focus in this conference is on Emerging Technologies for Developing Countries. The topics include: Practical method for evaluating the performance of a biometric algorithm;Software defined networking (SDN) for universal access;awale game: Application programming interface and augmented reality interface;vector space model of text classification based on inertia contribution of document;mood and personality influence on emotion;two parallelized filter methods for feature selection based on spark;a middleware for cyber physical systems in an internet of things environment: Case of for mobile asset tracking;signal processing, control and coordination in an intelligent connected vehicle;embedding a digital wallet to pay-with-a-selfie, from functional requirements to prototype;a predictive model for automatic generation control in smart grids using artificial neural networks;Exploring users’ continuance intention towards mobile SNS: A mobile value perspective;smartphone cyber security awareness in developing countries: A case of Thailand;development of an artificial neural network model for predicting surface water level: Case of Modder River catchment area;umobiTalk: Ubiquitous mobile speech based translator for Sesotho language.

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2nd international conference of the series Soft Computing: Theories and Applications, SoCTA 2017

2nd International conference of the series Soft Computing: T...

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2nd international conference of the series Soft Computing: Theories and Applications, SoCTA 2017

ISBN: (纸本)9789811305887

The proceedings contain 68 papers. The special focus in this conference is on Soft Computing: Theories and Applications. The topics include: Modified integral type weak contraction and common fixed point theorem with an auxiliary function;crop monitoring using IoT: A neural network approach;secure image steganography through pre-processing;understandable huffman coding: A case study;performance investigations of multi-resonance microstrip patch antenna for wearable applications;fuzzy counter propagation network for freehand sketches-based image retrieval;an efficient contrast enhancement technique based on firefly optimization;a review of computational swarm intelligence techniques for solving crypto problems;defense-in-depth approach for early detection of high-potential advanced persistent attacks;complexity metrics for component-based software system;An efficient algorithm to minimize EVM over ZigBee;performance measure of the proposed cost estimation model: Advance use case point method;fractal image compression and its techniques: A review;web services classification across cloud-based applications;performance evaluation and analysis of advanced symmetric key cryptographic algorithms for cloud computing security;A survey of CAD methods for tuberculosis detection in chest radiographs;static image shadow detection texture analysis by entropy-based method;A PSO algorithm-based task scheduling in cloud computing;a novel approach for target coverage in wireless sensor networks based on network coding;natural vibration of square plate with circular variation in thickness;Identification of relevant stochastic input variables for prediction of daily PM10 using artificial neural networks;majority-based classification in distributed environment;detection of advanced malware by machine learning techniques;Inter- and intra-scale dependencies-based CT image denoising in curvelet domain.

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