Voice Recognition for STEM Education Using Robotics

2017 ◽  
Author(s):  
Shubo Chen ◽  
Binsen Qian ◽  
Harry Cheng
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Stem Education ◽  
Basic Concept ◽  
Voice Recognition ◽  
Voice Control ◽  
K 12 ◽  
The Voice ◽  
Recognition Technique ◽  
Machine Learning Tool

In this paper, we provide a new voice recognition framework which allows K-12 students to write programs to solve problems using voice control. The framework contains the voice recognition module SPHINX which is based on an open source machine learning tool developed by Carnegie Mellon University and a wrapper function which is written in C/C++ interpreter Ch. The wrapper function allows students to interact the module in Ch. Along with Ch programming and robotic coursework, students will get the chance to learn the basic concept of machine learning and voice recognition technique. In order to bring students attention and interest in machine learning, various tasks have been designed for students to accomplish based on the framework. The framework is also flexible for them to explore other interesting projects.

scholarly journals An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0206409 ◽  
Author(s):  
Stephen Solis-Reyes ◽  
Mariano Avino ◽  
Art Poon ◽  
Lila Kari
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Learning Tool ◽  
Hiv 1 ◽  
Machine Learning Tool

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.

scholarly journals Comparison of CNNs and SVM for voice control wheelchair

2020 ◽  
Vol 9 (3) ◽  
pp. 387
Author(s):  
Mohammad Shahrul Izham Sharifuddin ◽  
Sharifalillah Nordin ◽  
Azliza Mohd Ali
Keyword(s):  
Feature Extraction ◽  
Processing Time ◽  
Voice Recognition ◽  
The Other ◽  
Raspberry Pi ◽  
Intelligent Wheelchair ◽  
Voice Control ◽  
Voice Data ◽  
The Voice ◽  
Feature Extraction Technique

In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+  to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.

scholarly journals A User- Based Recommendation with a Scalable Machine Learning Tool

2015 ◽  
Vol 5 (5) ◽  
pp. 1153 ◽  
Author(s):  
Ch. Veena ◽  
B. Vijaya Babu
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Open Source ◽  
Recommender Systems ◽  
Recommender System ◽  
Historical Data ◽  
Machine Learning Algorithms ◽  
Frame Work ◽  
Machine Learning Tool ◽  
Selection Of

Recommender Systems have proven to be valuable way for online users to recommend information items like books, videos, songs etc.colloborative filtering methods are used to make all predictions from historical data. In this paper we introduce Apache mahout which is an open source and provides a rich set of components to construct a customized recommender system from a selection of machine learning algorithms.[12] This paper also focuses on addressing the challenges in collaborative filtering like scalability and data sparsity. To deal with scalability problems, we go with a distributed frame work like hadoop. We then present a customized user based recommender system.

scholarly journals Automatic Vehicle Turn Indicator using Speech Recognition

2019 ◽  
Vol 8 (3) ◽  
pp. 6697-6700
Keyword(s):  
Regular Language ◽  
Voice Recognition ◽  
Human Communication ◽  
Signaling Systems ◽  
Turn Indicator ◽  
Voice Control ◽  
Language Control ◽  
Equipment Manufacture ◽  
Manual Tasks ◽  
The Voice

Voice-controlled innovation is an energizing region of research that is utilized to help people in the mechanical control of manual frameworks. It is a part of human-communication that builds the advances in programmed discourse acknowledgment or ASR with the inventive advances of characteristic language handling or NLP. Wise frameworks, for example, Automatic Vehicle Signaling Systems, can likewise take into consideration adaptability in manual activities. In late investigations, analysts have investigated regular language control of manual tasks. In this paper we will research interfacing voice control activities with Arduino-based equipment stages that are utilized to structure the programmed sign highlights in a vehicle. The control system includes a voice recognition circuit for activating turn signal devices within the vehicle. The voice recognition circuit takes in input from the voice from the Google maps voice assistant. In some formats, a wireless hardware is provided while in other embodiments original equipment manufacture is accommodated

RISK CALCULATION AND THREAT ASSESSMENT OF THE USE OF VOICE CONTROL SYSTEMS IN TRUSTED DISTRIBUTED COMPUTER SYSTEMS

2021 ◽  
pp. 93-104
Author(s):  
Александр Александрович Хайдаров ◽  
Александр Сергеевич Шишлов ◽  
Николай Николаевич Толстых
Keyword(s):  
Control Systems ◽  
Voice Recognition ◽  
Threat Assessment ◽  
Distributed Computer System ◽  
Dynamic Transformation ◽  
Algorithmic Support ◽  
Voice Control ◽  
Recognition Systems ◽  
The Stability ◽  
The Voice

Цель исследования состоит в повышении защищенности элементов распределенной компьютерной системы автоматического распознавания голосовых команд от возможного неверного определения команды за счет создания алгоритмического обеспечения оценки и регулирования рисков неверной идентификации голосовой команды для сравнения реализации двух алгоритмов: алгоритм динамической трансформации временной шкалы и алгоритм на основе скрытых Марковских процессов.Полученные результаты могут быть использованы или адаптированы при необходимости повышения стойкости систем автоматического распознавания голосовых команд на этапах проектирования и модернизации, а также при необходимости восстановления эффективности функционирования после компрометации или взлома. The aim of the study is to increase the security of the elements of a distributed computer system for automatic recognition of voice commands from possible incorrect identification of the command by creating algorithmic support for assessing and managing the risks of incorrect identification of the voice command to compare the implementation of two algorithms: the algorithm for dynamic transformation of the timeline and the algorithm based on hidden Markov processes.The obtained results can be used or adapted if it is necessary to increase the stability of automatic voice recognition systems at the design and modernization stages, as well as if it is necessary to restore the efficiency of functioning after a compromise or hacking.

scholarly journals Comparison between Physiological and Behavioral Characteristics of Biometric System

2019 ◽  
Vol 54 (6) ◽  
Author(s):  
Azal Habeeb
Keyword(s):  
Iris Recognition ◽  
Voice Recognition ◽  
Technical Aspect ◽  
Vital Role ◽  
Biometric System ◽  
Behavioral Biometrics ◽  
Canny Edge ◽  
Mel Frequency Cepstral Coefficient ◽  
The Voice ◽  
Recognition Technique

Biometrics is a technical aspect to identify each person from others. It is one of the ways to distinguish a person’s identity. The biometric system plays a vital role in data security. There are two types of biometric systems, i.e., physiological and behavioral biometrics. Physiological biometrics involves the fingerprint, iris, and face, while behavioral biometrics includes the signature, stroke, and voice. This paper discussed the iris recognition technique using the Canny edge detector and Hough transform to separate iris region from the eye images. The voice recognition technique was discussed using mel-frequency cepstral coefficient (MFCC) method. Finally, the paper compared iris recognition and voice recognition according to their properties and their performance.

The Design and Realization of Remote Voice Control System Based on the Intelligent Home

2014 ◽  
Vol 530-531 ◽  
pp. 1112-1118
Author(s):  
Ye Fen Yang ◽  
Jun Zhang ◽  
Dong Hai Zeng
Keyword(s):  
Control System ◽  
Mobile Phone ◽  
Control Function ◽  
Voice Recognition ◽  
Security Requirements ◽  
Intelligent Home ◽  
Design Program ◽  
Wide Range ◽  
Voice Control ◽  
The Voice

A design program of remote voice control system is presented based on the intelligent home on Android mobile phone platform. Via the voice recognition of Android mobile phone, the intelligent home can have a remote voice control function by this program, which greatly improves the security requirements of the intelligent home. This system is tested and proved its real-time, effectiveness and stability. Meanwhile, it can also provide a practical reference solution for human-computer interaction, having a wide range of application.

scholarly journals An Analysis of Automatic Voice Recognition and Speaker Identification Algorithms and its Applications

2018 ◽  
Vol 7 (2.24) ◽  
pp. 415
Author(s):  
Ram Sethuraman ◽  
J Selvin Paul Peter ◽  
Shanthan Reddy Middela ◽  
. .
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Speaker Identification ◽  
Learning Algorithms ◽  
Voice Recognition ◽  
Machine Learning Algorithms ◽  
The Voice

Voice recognition is the domain which is used to identify the speaker behind a speech through their voice. In the field of research, Voice recognition is a domain which has been widely explored by data mining experts and used for various applications. The features of the voice are extracted through methods like MFCC and then various Data Mining and Machine learning algorithms are applied for each specific application. Researchers have explored and tested the efficiencies of various algorithms for various purposes. There appears to be specific algorithms which outperform the rest in certain applications whereas they tend to perform badly for certain other applications. This paper aims to discuss the various Voice recognition techniques and its uses in various domains. The work aims in providing the characteristics and limitations of these approaches. 

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