scholarly journals DGR: Deep Gender Recognition of Human Speech

2019 ◽  
Author(s):  
Rami S. Alkhawaldeh
Keyword(s):  
Feature Selection ◽  
Voice Recognition ◽  
Support Vector ◽  
Classification Models ◽  
Training Set ◽  
Human Voice ◽  
Voice Signal ◽  
Signal Features ◽  
Selection Algorithms ◽  
The Voice

The speech entailed in human voice comprises essentially para-linguistic information used in many voice-recognition applications. Gender voice-recognition is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e, male or female) from a voice signal. The contributions are involved in two folds: (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. Experimental results show the importance of sub-features over others, which are vital for enhancing the efficiency of classification models performance. Experimentation reveals that the best recall value is equal to 99.97%; 99.7% of two models of Deep Learning (DL) and Support Vector Machine (SVM) and with feature selection the best recall value is 100% for SVM techniques.

scholarly journals DGR: Gender Recognition of Human Speech Using One-Dimensional Conventional Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Rami S. Alkhawaldeh
Keyword(s):  
Feature Selection ◽  
Support Vector ◽  
Classification Models ◽  
Training Set ◽  
Human Voice ◽  
Voice Signal ◽  
Signal Features ◽  
Paralinguistic Information ◽  
Selection Algorithms ◽  
The Voice

The speech entailed in human voice comprises essentially paralinguistic information used in many voice-recognition applications. Gender voice is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e., male or female) from a voice signal. The contributions are three-fold including (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. The experimental results show the importance of subfeatures over others, which are vital for enhancing the efficiency of classification models’ performance. Experimentation reveals that the best recall value is equal to 99.97%; the best recall value is 99.7% for two models of deep learning (DL) and support vector machine (SVM), and with feature selection, the best recall value is 100% for SVM techniques.

scholarly journals A Novel automatic voice recognition system based on text-independent in a noisy environment

2020 ◽  
Vol 10 (4) ◽  
pp. 3643
Author(s):  
Motaz Hamza ◽  
Touraj Khodadadi ◽  
Sellappan Palaniappan
Keyword(s):  
Voice Recognition ◽  
Recognition System ◽  
Primary Objective ◽  
Training Data ◽  
Gaussian Mixtures ◽  
Noisy Environment ◽  
Human Voice ◽  
Voice Signal ◽  
Noise Filter ◽  
The Voice

Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user to speak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. The voice recognition was done in two steps: training and testing. During the training a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisy environment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noise methods were applied, we investigated different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that subsequently improved the performance of the automatic voice recognition system.

BREAST CANCER DETECTION USING RSFS-BASED FEATURE SELECTION ALGORITHMS IN THERMAL IMAGES

2021 ◽  
pp. 2150020
Author(s):  
Nazila Darabi ◽  
Abdalhossein Rezai ◽  
Seyedeh Shahrbanoo Falahieh Hamidpour
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Cancer Detection ◽  
Vital Role ◽  
Support Vector ◽  
Computer Aided Detection ◽  
K Nearest Neighbors ◽  
Cad System ◽  
Common Cancer ◽  
Selection Algorithms

Breast cancer is a common cancer in female. Accurate and early detection of breast cancer can play a vital role in treatment. This paper presents and evaluates a thermogram based Computer-Aided Detection (CAD) system for the detection of breast cancer. In this CAD system, the Random Subset Feature Selection (RSFS) algorithm and hybrid of minimum Redundancy Maximum Relevance (mRMR) algorithm and Genetic Algorithm (GA) with RSFS algorithm are utilized for feature selection. In addition, the Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) algorithms are utilized as classifier algorithm. The proposed CAD system is verified using MATLAB 2017 and a dataset that is composed of breast images from 78 patients. The implementation results demonstrate that using RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 85.36% and 75%, and sensitivity of 94.11% and 79.31%, respectively. In addition, using hybrid GA and RSFS algorithm for feature selection and kNN and SVM algorithms as classifier have accuracy of 83.87% and 69.56%, and sensitivity of 96% and 81.81%, respectively, and using hybrid mRMR and RSFS algorithms for feature selection and kNN and SVM algorithms as classifier have accuracy of 77.41% and 73.07%, and sensitivity of 98% and 72.72%, respectively.

Using the Text Categorization Framework for Protein Classification

2010 ◽  
pp. 128-140 ◽  
Author(s):  
Ricco Rakotomalala ◽  
Faouzi Mhamdi
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Predictive Model ◽  
Text Categorization ◽  
Learning Algorithm ◽  
Support Vector ◽  
Protein Classification ◽  
Fixed Length ◽  
Selection Algorithms ◽  
Proteins Classification

In this chapter, we are interested in proteins classification starting from their primary structures. The goal is to automatically affect proteins sequences to their families. The main originality of the approach is that we directly apply the text categorization framework for the protein classification with very minor modifications. The main steps of the task are clearly identified: we must extract features from the unstructured dataset, we use the fixed length n-grams descriptors; we select and combine the most relevant one for the learning phase; and then, we select the most promising learning algorithm in order to produce accurate predictive model. We obtain essentially two main results. First, the approach is credible, giving accurate results with only 2-grams descriptors length. Second, in our context where many irrelevant descriptors are automatically generated, we must combine aggressive feature selection algorithms and low variance classifiers such as SVM (Support Vector Machine).

Feature Selection Algorithm for Hyperlipidemia Classification

2014 ◽  
Vol 701-702 ◽  
pp. 110-113
Author(s):  
Qi Rui Zhang ◽  
He Xian Wang ◽  
Jiang Wei Qin
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Information Gain ◽  
Classification Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Document Frequency ◽  
Selection Algorithms ◽  
Term Weights

This paper reports a comparative study of feature selection algorithms on a hyperlipimedia data set. Three methods of feature selection were evaluated, including document frequency (DF), information gain (IG) and aχ2 statistic (CHI). The classification systems use a vector to represent a document and use tfidfie (term frequency, inverted document frequency, and inverted entropy) to compute term weights. In order to compare the effectives of feature selection, we used three classification methods: Naïve Bayes (NB), k Nearest Neighbor (kNN) and Support Vector Machines (SVM). The experimental results show that IG and CHI outperform significantly DF, and SVM and NB is more effective than KNN when macro-averagingF1 measure is used. DF is suitable for the task of large text classification.

scholarly journals Predicting the DPP-IV Inhibitory ActivitypIC50Based on Their Physicochemical Properties

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Tianhong Gu ◽  
Xiaoyan Yang ◽  
Minjie Li ◽  
Milin Wu ◽  
Qiang Su ◽  
...  
Keyword(s):  
Feature Selection ◽  
Physicochemical Properties ◽  
Inhibitory Activity ◽  
Molecular Descriptors ◽  
Feature Selection Method ◽  
Development Program ◽  
Support Vector ◽  
Training Set ◽  
Second Development ◽  
Dpp Iv

The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50of the DPP-IV inhibitors as described in this paper has been given in the introduction.

scholarly journals CAMPUS MITHRA: Design and implementation of voice based attender robot

2021 ◽  
Vol 2115 (1) ◽  
pp. 012006
Author(s):  
Ambreen Saniya ◽  
M S Chandana ◽  
Maria Sharon Dennis ◽  
K Pooja ◽  
D J Chaithanya ◽  
...  
Keyword(s):  
Natural Language ◽  
Voice Recognition ◽  
Embedded Control ◽  
Railway Stations ◽  
Human Voice ◽  
Domains Of Life ◽  
The University ◽  
The Voice ◽  
University Setting ◽  
Line Following

Abstract A robot is a machine which is programmed by a computer and the movements and functions of which are controlled by an external or an embedded control. It has dynamic uses in all domains of life. A robot in a university setting can be used as an attender for passing circulars around instead of multiple attenders doing the task manually which is a cost and time consuming process. Parents often find it difficult to navigate through the unfamiliar university. In this paper, we have focused on a voice based attender robot with line following capabilities along with speech recognition that can be used at universities for a variety of purposes like passing around the circulars, interacting with parents and helping them navigate through the university through Spoken Natural Language. The main objectives of the proposed work is to reduce the burden of passing circulars, calling a student/faculty on the attender by designing a robot that is also competent enough to connect with human through spoken natural language such as English or Kannada, so that it interacts with parents who are new to the institution and do not know whom to approach. The main aim of this work is to introduce a robot that it is able to interact with human through the Spoken Natural Language. Here, the focus is on two languages; English and Kannada. This system uses a voice recognition module to recognize human voice and a voice playback module is used to reply back in either English or Kannada according to the user’s command. It can work in two modes, the voice recognition mode to answer to user queries or in line following mode to pass circulars, call student/faculty. In this way, the voice based attender robot finds its applications in the university setting. But it is not limited to only universities. It can also be further implemented in places like railway stations, bus stations, big factories and other similar surroundings.

scholarly journals A Novel User Interface for Text Dependent Human Voice Recognition System

2018 ◽  
Vol 7 (4.6) ◽  
pp. 258
Author(s):  
Ramadevi P ◽  
. .
Keyword(s):  
User Interface ◽  
Speech Processing ◽  
Voice Recognition ◽  
Recognition System ◽  
Noise Elimination ◽  
Human Voice ◽  
Voice Signal ◽  
Band Pass ◽  
The Given

In an effort to provide a more efficient representation of the speech signal, the application of the wavelet analysis is considered. This research presents an effective and robust method for extracting features for speech processing. Here, we proposed a novel user interface for Text Dependent Human Voice Recognition (TD-HVR) system. The proposed HVR model utilizes decimated bi-orthogonal wavelet transform (DBT) approach to extract the low level features from the given input voice signal, then the noise elimination will be done by band pass filtering followed by normalization for better quality of a voice signal and finally the formants of a train and test voices will be calculated by using the Additive Prognostication (AP) algorithm. Simulation results have been compared with the existing HVR schemes, and shown that the proposed user interface system has performed superior to the conventional HVR systems with an accuracy rate of approximately 99 %.  

scholarly journals Voice Recognition System Through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 4478-4483
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Voice Recognition ◽  
Recognition System ◽  
New Method ◽  
Human Voice ◽  
Developing Area ◽  
Voice Processing ◽  
The Voice

Human voice recognition by computers has been ever developing area since 1952. It is challenging task for a computer to understand and act according to human voice rather than to commands or programs. The reason is that no two human’s voice or style or pitch will be similar and every word is not pronounced by everyone in a similar fashion. Background noises and disturbances may confuse the system. The voice or accent of the same person may change according to the user’s mood, situation, time etc. despite of all these challenges, voice recognition and speech to text conversion has reached a successful stage. Voice processing technology deserves still more research. As a tip of iceberg of this research we contribute our work on this are and we propose a new method i.e., VRSML (Voice Recognition System through Machine Learning) mainly focuses on Speech to text conversion, then analyzing the text extracted from speech in the form of tokens through Machine Learning. After analyzing the derived text, reports are created in textual as well graphical format to represent the vocabulary levels used in that speech. As Supervised learning algorithm from Machine Learning is employed to classify the tokens derived from text, the reports will be more accurate and will be generated faster.

Ensemble incremental deep multiple layer perceptron model – sentiment analysis application

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Renuka Devi D. ◽  
Sasikala S.
Keyword(s):  
Feature Selection ◽  
Support Vector ◽  
Data Sets ◽  
Nearest Neighbour ◽  
Swarm Optimization ◽  
Content Type ◽  
Twitter Data ◽  
Proposed Model ◽  
Hoeffding Tree ◽  
Selection Algorithms

Purpose The purpose of this paper is to enhance the accuracy of classification of streaming big data sets with lesser processing time. This kind of social analytics would contribute to society with inferred decisions at a correct time. The work is intended for streaming nature of Twitter data sets. Design/methodology/approach It is a demanding task to analyse the increasing Twitter data by the conventional methods. The MapReduce (MR) is used for quickest analytics. The online feature selection (OFS) accelerated bat algorithm (ABA) and ensemble incremental deep multiple layer perceptron (EIDMLP) classifier is proposed for Feature Selection and classification. Three Twitter data sets under varied categories are investigated (product, service and emotions). The proposed model is compared with Particle Swarm Optimization, Accelerated Particle Swarm Optimization, accelerated simulated annealing and mutation operator (ASAMO). Feature Selection algorithms and classifiers such as Naïve Bayes, support vector machine, Hoeffding tree and fuzzy minimal consistent class subset coverage with the k-nearest neighbour (FMCCSC-KNN). Findings The proposed model is compared with PSO, APSO, ASAMO. Feature Selection algorithms, and classifiers such as Naïve Bayes (NB), support vector machine (SVM), Hoeffding Tree (HT), and Fuzzy Minimal Consistent Class Subset Coverage with the K-Nearest Neighbour (FMCCSC-KNN). The outcome of the work has achieved an accuracy of 99%, 99.48%, 98.9% for the given data sets with the processing time of 0.0034, 0.0024, 0.0053, seconds respectively. Originality/value A novel framework is proposed for Feature Selection and classification. The work is compared with the authors’ previously developed classifiers with other state-of-the-art Feature Selection and classification algorithms.

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