Mr. Jonathan T. Harris Profile

Jonathan T. Harris

at Univ of Central Florida

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 May 2016 Paper

Technological evaluation of gesture and speech interfaces for enabling dismounted soldier-robot dialogue

Ravi Kiran Kattoju, Daniel Barber, Julian Abich, Jonathan Harris

Proceedings Volume 9837, 98370N (2016) https://doi.org/10.1117/12.2223894

KEYWORDS: Speech recognition, Visualization, Intelligence systems, Gesture recognition, Robots, Resistors, Sensors, Error analysis, Data processing, Analytical research

Read Abstract +

With increasing necessity for intuitive Soldier-robot communication in military operations and advancements in interactive technologies, autonomous robots have transitioned from assistance tools to functional and operational teammates able to service an array of military operations. Despite improvements in gesture and speech recognition technologies, their effectiveness in supporting Soldier-robot communication is still uncertain. The purpose of the present study was to evaluate the performance of gesture and speech interface technologies to facilitate Soldier-robot communication during a spatial-navigation task with an autonomous robot. Gesture and speech semantically based spatial-navigation commands leveraged existing lexicons for visual and verbal communication from the U.S Army field manual for visual signaling and a previously established Squad Level Vocabulary (SLV). Speech commands were recorded by a Lapel microphone and Microsoft Kinect, and classified by commercial off-the-shelf automatic speech recognition (ASR) software. Visual signals were captured and classified using a custom wireless gesture glove and software. Participants in the experiment commanded a robot to complete a simulated ISR mission in a scaled down urban scenario by delivering a sequence of gesture and speech commands, both individually and simultaneously, to the robot. Performance and reliability of gesture and speech hardware interfaces and recognition tools were analyzed and reported. Analysis of experimental results demonstrated the employed gesture technology has significant potential for enabling bidirectional Soldier-robot team dialogue based on the high classification accuracy and minimal training required to perform gesture commands.

Proceedings Article | 3 June 2014 Paper

Speech and gesture interfaces for squad-level human-robot teaming

Jonathan Harris, Daniel Barber

Proceedings Volume 9084, 90840B (2014) https://doi.org/10.1117/12.2052961

KEYWORDS: Intelligence systems, Visualization, Reconnaissance, Buildings, Gesture recognition, Surveillance, Speech recognition, Associative arrays, Commercial off the shelf technology, Analytical research

Read Abstract +

As the military increasingly adopts semi-autonomous unmanned systems for military operations, utilizing redundant and intuitive interfaces for communication between Soldiers and robots is vital to mission success. Currently, Soldiers use a common lexicon to verbally and visually communicate maneuvers between teammates. In order for robots to be seamlessly integrated within mixed-initiative teams, they must be able to understand this lexicon. Recent innovations in gaming platforms have led to advancements in speech and gesture recognition technologies, but the reliability of these technologies for enabling communication in human robot teaming is unclear. The purpose for the present study is to investigate the performance of Commercial-Off-The-Shelf (COTS) speech and gesture recognition tools in classifying a Squad Level Vocabulary (SLV) for a spatial navigation reconnaissance and surveillance task. The SLV for this study was based on findings from a survey conducted with Soldiers at Fort Benning, GA. The items of the survey focused on the communication between the Soldier and the robot, specifically in regards to verbally instructing them to execute reconnaissance and surveillance tasks. Resulting commands, identified from the survey, were then converted to equivalent arm and hand gestures, leveraging existing visual signals (e.g. U.S. Army Field Manual for Visual Signaling). A study was then run to test the ability of commercially available automated speech recognition technologies and a gesture recognition glove to classify these commands in a simulated intelligence, surveillance, and reconnaissance task. This paper presents classification accuracy of these devices for both speech and gesture modalities independently.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years