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A Speech Recognition Linear Systems Lab

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

New ECE laboratories

Tagged Division

Electrical and Computer

Tagged Topic


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Paper Authors


William Joseph Ebel Sr. Saint Louis University

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Dr. William J. Ebel received his Ph.D. degree from the University of Missouri - Rolla in 1991 in Electrical Engineering. He joined St. Louis University in the Fall of 2000 as an Associate Professor of Electrical Engineering and has served as the department Chairman for two different periods. He teaches in the areas of signal and image processing, communications, and robotics and he is actively involved in novel instructional methods. His research interests include image processing and robotics.

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Chris Carroll Saint Louis University Orcid 16x16

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Dr. Carroll is an Assistant Professor and the Civil Engineering Program Coordinator in Parks College of Engineering, Aviation and Technology at Saint Louis University. His experimental research interests focus on reinforced and prestressed concrete, while his engineering education research interests focus on experiential learning at both the university and K-12 levels. Dr. Carroll is the chair of ACI Committee S802 - Teaching Methods and Educational Materials and he has been formally engaged in K-12 engineering education for nearly ten years.

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In this paper, a Linear Systems laboratory project is described that involves designing a simplified speech recognition system to recognize the 5 long vowel sounds for a team of students. This laboratory project has several goals including (1) to solve an engineering problem using the frequency domain, (2) practice using the matlab language and development environment, and (3) provide a setting where a number of ABET outcomes can be practiced and measured, specifically Student Outcomes (a), (b), (d), (e), (h) and (k). This is a first semester, junior year laboratory that is a co-requisite of a standard Linear Systems course.

The students are directed to use a solution strategy that involves collecting training data, converting the training data into the frequency domain, analyzing the frequency domain data, and developing a decision tree that implements the decision logic. The decision tree nodes use spectral energy in specific frequency bands to create a metric vector and use support vectors to define decision regions. The nearest neighbor decision rule is used to select a path in the decision tree.

To provide a real-world context to this engineering design, student teams are put together that mix accents and gender in order to provide a diverse data set for each group. Besides determining system performance, students are required to perform a simple market study to estimate the number of potential users assuming that their system performance translates to the performance of a full speech recognition system. This requires some research into the number of English speaking persons with accents from the team and also with consideration of their system performance.

Ebel, W. J., & Carroll, C. (2019, June), A Speech Recognition Linear Systems Lab Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--31999

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