The Voice-Recognition Accuracy of Blind Listeners

Perception ◽  
1983 ◽  
Vol 12 (2) ◽  
pp. 223-226 ◽  
Author(s):  
Ray Bull ◽  
Harriet Rathborn ◽  
Brian R Clifford
Keyword(s):  
Recognition Accuracy ◽  
Age At Onset ◽  
Voice Recognition ◽  
Research Programme ◽  
The Voice

A research programme has been carried out that concerns the accuracy with which listeners can identify a speaker heard once before. The present study examined the voice-recognition abilities of blind listeners, and it was found that they could more accurately select target voices from the test arrays than could sighted people. However, the degree of blindness, the age at onset of blindness, and the number of years of blindness all failed to relate to voice-recognition accuracy.

Lampu Sein Helm Sepeda Berbasis Voice Recognition

2019 ◽  
Vol 11 (01) ◽  
pp. 20-25
Author(s):  
Indra Saputra ◽  
Parulian Silalahi ◽  
Bayu Cahyawan ◽  
Imam Akbar
Keyword(s):  
Voice Recognition ◽  
Driving Safety ◽  
Bicycle Helmet ◽  
Turn Signal ◽  
Voice Command ◽  
The Voice ◽  
Indicator Signal

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of <2 meters with noise <71 db.

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

Voice Assistant — Application of Speech Recognition Technology in the Android System

2014 ◽  
Vol 596 ◽  
pp. 384-387
Author(s):  
Ge Liu ◽  
Hai Bing Zhang
Keyword(s):  
Speech Recognition ◽  
Service Providers ◽  
Voice Recognition ◽  
Working Process ◽  
Bottleneck Problems ◽  
Speech Recognition Technology ◽  
Android System ◽  
The Voice

This paper introduces the concept of Voice Assistant, the voice recognition service providers, several typical Voice Assistant product, and then the basic working process of the Voice Assistant is described in detail and proposed the technical bottleneck problems in the development of Voice Assistant software.

scholarly journals Design and Fabrication of Voice Controlled Unmanned Aerial Vehicle

2016 ◽  
Vol 5 (3) ◽  
pp. 205
Author(s):  
S. Sakthi Anand ◽  
R. Mathiyazaghan
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Voice Recognition ◽  
Search And Rescue ◽  
Voice Input ◽  
Recognition Process ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
The Voice

<p class="Default">Unmanned Aerial Vehicles have gained well known attention in recent years for a numerous applications such as military, civilian surveillance operations as well as search and rescue missions. The UAVs are not controlled by professional pilots and users have less aviation experience. Therefore it seems to be purposeful to simplify the process of aircraft controlling. The objective is to design, fabricate and implement an unmanned aerial vehicle which is controlled by means of voice recognition. In the proposed system, voice commands are given to the quadcopter to control it autonomously. This system is navigated by the voice input. The control system responds to the voice input by voice recognition process and corresponding algorithms make the motors to run at specified speeds which controls the direction of the quadcopter.</p>

THE EFFECT OF DIFFERENT OCCUPATIONAL BACKGROUND NOISES ON VOICE RECOGNITION ACCURACY

2022 ◽  
pp. 1-30
Author(s):  
Song Li ◽  
Mustafa Ozkan Yerebakan ◽  
Yue Luo ◽  
Ben Amaba ◽  
William Swope ◽  
...  
Keyword(s):  
Background Noise ◽  
Recognition Accuracy ◽  
Voice Recognition ◽  
Recognition Algorithm ◽  
Control Mechanisms ◽  
Recognition Algorithms ◽  
Significant Performance ◽  
Industrial Productivity ◽  
Overall Performance ◽  
Occupational Background

Abstract Voice recognition has become an integral part of our lives, commonly used in call centers and as part of virtual assistants. However, voice recognition is increasingly applied to more industrial uses. Each of these use cases has unique characteristics that may impact the effectiveness of voice recognition, which could impact industrial productivity, performance, or even safety. One of the most prominent among them is the unique background noises that are dominant in each industry. The existence of different machinery and different work layouts are primary contributors to this. Another important characteristic is the type of communication that is present in these settings. Daily communication often involves longer sentences uttered under relatively silent conditions, whereas communication in industrial settings is often short and conducted in loud conditions. In this study, we demonstrated the importance of taking these two elements into account by comparing the performances of two voice recognition algorithms under several background noise conditions: a regular Convolutional Neural Network (CNN) based voice recognition algorithm to an Auto Speech Recognition (ASR) based model with a denoising module. Our results indicate that there is a significant performance drop between the typical background noise use (white noise) and the rest of the background noises. Also, our custom ASR model with the denoising module outperformed the CNN based model with an overall performance increase between 14-35% across all background noises. . Both results give proof that specialized voice recognition algorithms need to be developed for these environments to reliably deploy them as control mechanisms.

INFORMATION SYSTEM FOR PEOPLE WITH HEARING IMPAIRMENT

2021 ◽  
Vol 335 (1) ◽  
pp. 54-59
Author(s):  
Y.S. Nurakhov ◽  
A.E. Kami
Keyword(s):  
Quality Of Life ◽  
Information System ◽  
Public Life ◽  
Voice Recognition ◽  
Hearing Impairments ◽  
Hardware Complex ◽  
Software And Hardware ◽  
Functional Blocks ◽  
The Voice

The article presents the development of an information system for recognizing voice into text for people with hearing impairments, which makes it possible to improve the quality of life and interaction in society with other people. The device, software, functional blocks and subsystems of the information system are described. Examples of possible application and placement of the system in various spheres of public life are given. One of the types of implementation of the voice recognition information system is described. The development and creation of prototypes of a device for people with hearing impairments is considered. In the course of the research, the Google Speech Api technology was selected for speech recognition. In addition, this article presents a software and hardware complex that allows you to translate speech into text and then display it on the screen. Arduino UNO-based devices were chosen to achieve the goal. All information is processed on the smartphone of people with hearing impairments, which is sent to the device via Bluetooth with Arduino.

A Comparison of Three Methods for Controlling Aircraft Systems

1986 ◽  
Vol 30 (7) ◽  
pp. 638-641
Author(s):  
John P. Zenyuh ◽  
John M. Reising
Keyword(s):  
Control Systems ◽  
Relative Effectiveness ◽  
Voice Recognition ◽  
Recognition System ◽  
Control Device ◽  
Control Mode ◽  
Aircraft Systems ◽  
Remote Touch ◽  
The Voice ◽  
Speed And Accuracy

The objective of this study was to compare the relative effectiveness of three modes of subsystem control: a voice recognition system with visual feedback presented on the head-up display, a standard multifunction control device with tailored switching logic, and a remotely operated multifunction control with feedback presented on the head-up display. Comparisons were based on measures of interference with a loading task and overall speed and accuracy of the control operations performed. The working hypothesis was that the voice system and head-up multifunction control would manifest substantially lower interference with the primary task, while subsystem control operation times would remain unaffected by control mode. The results indicate that performance with the remote touch panel was significantly poorer than with the voice or standard multifunction control systems.

scholarly journals Smart wheelchair using android smartphone for physically disabled people

2018 ◽  
Vol 7 (2) ◽  
pp. 453 ◽  
Author(s):  
Siti Nur Suhaila Mirin ◽  
Khalil Azha Mohd Annuar ◽  
Chai Pui Yook
Keyword(s):  
Embedded System ◽  
Voice Recognition ◽  
Disabled People ◽  
Voice Input ◽  
Left Turn ◽  
Physically Disabled ◽  
The Right ◽  
Physically Disabled People ◽  
The Voice ◽  
Smart Wheelchair

This paper describes the development of a smart wheelchair system with voice recognition and touch controlled using an embedded system. An android application is developed and installed on the android smartphone. The system is divided into two main modes: voice recognition mode and touch mode. For the voice recognition mode, elderlies or physically disabled people (users) can provide the voice input, for example, “go”, “reverse”, “turn to the left”, “turn to the right” and “stop”. The wheelchair will move according to the command given. For the touch mode, the user can select the specified direction displayed within the four quadrants on the screen of the android smartphone to control the wheelchair. An Arduino Uno is used to execute all commands. The MD30C motor driver and HC05 Bluetooth module are used in this system. This system is designed to save time and energy of the user.

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