LYNDON REY
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ACADEMIA
CONSULTING
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Lyndon Rey
Student, linguist, and AI researcher, focusing on acoustic phonetic analysis, speech modeling with machine learning, and topic/sentiment extraction.
Currently: M.A. Student, University of Calgary
EMAIL: [email protected]
GITHUB: lyndonrey
LINKEDIN: Lyndon Rey
INSTAGRAM: @lyndon_t_rey
Welcome! My name is Lyndon, and I'm currently a second-year Masters of Arts student studying at the University of Calgary. My interests are in computational modeling and acoustic phonetics; in general, I study the interface of acoustic phonetics with artificial intelligence.

Outside of school, I play fullback for the University of Calgary Dinos Men's Rugby team.
My current thesis work is looking at whether there are any acoustic correlates which cause speech to be perceived as more honest.

In Fall 2019, I am TA for LING 201.03 and LING 202 - if you are a student in these courses and are looking to contact me, please email me at my ucalgary email address.

This fall, I am continuing my work in the Second Language Acquisition lab run by Dr. S. Carroll, as a data analyst and phonetician.
I am available to consult on a variety of linguistics-related topics, particularly involving computational linguistics or natural language processing.

My educational background in the liberal arts, combined with industry software development and machine learning experience, allows me to approach tasks uniquely and effectively. I aim to provide quantifiably valuable machine learning- and data-based solutions to the very human problems found in modern consumer-facing applications.

My methods typically involve statistical analysis of language using either R or Python. Machine learning-wise, I have industry-level compentence working with Python machine learning libraries including Tensorflow, SciKit-learn, and PyTorch. Finally, I provide human-understandable explanations of my solutions, while explaining specifically how they directly bring value to a company.

Specifically, I have industry-capable skills in the following areas. For consulting inquiries, please email me at [email protected]

Text-Based Natural Language Processing:
- Sentiment extraction
- Topic recognition
- Linguistic analysis of social media data
- Computational analysis of large corpora

Acoustic Analysis:
- Machine learning approaches to spoken speech
- Accent and speaker recognition
- Acoustic editing and modification of speech

Education


M.A. Linguistics, University of Calgary (2018-Present)
Thesis: To be Determined
Supervisor: Dr. Stephen Winters


B.A. Honours Linguistics, University of Western Ontario (2013-2017)
Thesis: Automatic Documentation of Vowels in Faetar: A Machine Learning Approach
UWO Gold Medal: Highest GPA in program
USports Academic All Canadian: Men's Curling


Recent Industry Work


Speech Science Intern, Nuance Communications Inc. (2018)
Researched, designed, and implemented unsupervised topic discovery and exploratory data analysis
Gained industry experience in acoustics, corpus linguistics, and machine learning
Acquired industry-level competence using Scikit-learn, numpy, pandas, etc.


Recent Publications


Rey, L., and Nagy, N. (2019). Documentation automatique de [i] en faetar : Une méthodologie pour la découverte de l'espace vocalique à l'aide de réseaux neuronaux artificiels. Geolinguistique. Online.

Rey, L. (2016). The Language Identification Problem: Formant Analysis and Cross-Linguistic Uniqueness, Western Papers in Linguistics / Cahiers linguistiques de Western: Vol. 3 , Article 5.

Selected Conference Presentations


Rey, L. (2019). Q-Theory Extended: Representing Tone Contours as Continuous Functions. Canadian Linguistics Association Annual Congress 2019, University of British Columbia

Rey, L. (2017). Finding Hidden Patterns through Machine Learning: A Methodology Measuring Vowel Variation in Corpora of Untagged Speech. ABLT/BWTL 2017, University of Toronto

Rey, L. (2017). A Neural Network Approach to Statistical Machine Learning, using Natural Speech. WISSLR 2017, Western University

Rey, L. (2017). Quantifying Variation in Heritage Faetar Speakers: A Neural Network Approach. TULCON 2017, University of Toronto