Resume


Spencer W. Jensen

+1 (801) 400-6720 spencerwjensen96@gmail.com

Education

MSc of Speech and Language Processing

📍University of Edinburgh 

🏴󠁧󠁢󠁳󠁣󠁴󠁿Edinburgh, Scotland

Ranked one of the world's best for speech and language processing

Courses: Speech Synthesis, Automatic Speech Recognition, NLP, Machine Translation, ML and Pattern Recognition, Second Language Acquisition, Speech Processing

Dissertation: Accentedness of L2 speech and L2 speech synthesis

MSc of Information Systems Management
BS in Information Systems, Mathematics Minor

📍Brigham Young University – Marriott School of Business

🇺🇸Provo, Utah, USA

Courses: Full Stack Development, Advanced DB, Mathematical Cryptography, Project Management, Fraud Detection and Prevention, PhD prep

Capstone: Managing digital advertising data using smart contracts and blockchain technology

Experience

Self-employed Contractor

Speech understanding, TTS and ASR decision making, data work, software development, web dev, home improvement and renovation projects 


Software Engineer Full-Stack

Rosetta Stone / IXL Learning


Associate Software Engineer Full-Stack

Right Angle Engineering


Student Software Engineering Team Lead/Research Assistant

Brigham Young University


JavaScript Developer

LoomVue – King’s Peak Technology


General Manager and Technology Specialist

JB Shelving and Kitchen Wise of Salt Lake City


Vice President of Student Experience

Association of Information Systems, Brigham Young University

Skills & Achievements

Entrepreneur: Self-employed contractor, developer, website designer and home improvement

Tech: Python, Pytorch, Azure, AWS, C#/.NET, Angular, React, SQL, Docker, JS/TS, Terraform, Bash, DBT, DB schemas, React, Node.js, Flutter, Java, LaTeX, Smart contract development, Testing

Advanced Czech, Intermediate Swedish, Beginner Russian, excellent interpretation and translation skills

2nd place AIS national Blockchain case competition, 2nd place Software Innovation competition in USA

State Champion in athletics, Eagle Scout, Musician - Euphonium player in brass band


Popular posts from this blog

Phonetic Posteriorgrams

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For my MSc dissertation, I am trying to synthesize speech with variable L2 accent strength. There are a few ways to approach this. The "fastest" and "easiest" way is to have the perfect dataset that has reliable labels for accent strength on each utterance. There are two problems with this approach. First, this dataset would be incredibly difficult and expensive to create. Second, it is difficult to get a trusted label for "accent strength" that is also consistent across the dataset. What does accent strength even mean and how would it be measured non-subjectively? I was talking about this problem with one of the PhD candidates in the CSTR group, and she told me to look into phonetic posteriorgrams. Since then, I have tried to read a bit about them. I wanted to explain them in this post.  What is a Phonetic Posteriorgram? A phonetic posteriorgram (PPG) is well described in it's name. Phonetic indicates that we are talking about phonemes, or speech cate...
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The Hard Part About Speech

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The rise of Large Language Models (LLM) in recent years has certainly made a lot of people see the world differently. I believe that many people are more interested in statistics, machine learning, computer science, and linguistics because of these fantastic advancements, myself included. As I have studied intensely at the University of Edinburgh over the last year, I have realized more about what these kinds of models do, and how they relate to the bigger picture of modeling functions in general. All of these models are doing some form of function approximation/prediction task. Language models like GPT or BERT are simply predicting the next word token given the context, Automatic Speech Recognizers (ASR) like Whisper are predicting the text output given a waveform, Text to Speech (TTS) models like Amazon Polly are trying to predict the spectrogram or waveform given the textual input and who it should sound like. There is always a task, a dependency, data, and a metric to measure that ...
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Language Identification Project

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I wanted to complete some practice problems and build a small basic portfolio of small tech projects that showcase some of my recent learning at the University of Edinburgh. I have taken several courses about natural language processing (NLP) at uni.  NLP is a very wide term for a variety of different tasks, for example, text classification, named entity recognition, machine translation, language modeling, etc. This project was just a simple text classification task. I wanted to input text and have a model predict which language it is. Find the code on my  Github , and my steps that I took to create the project below. Hope this helps facilitate your learning too. Step 1: Find data Foundational to any project involving any sort of prediction. You need data and preferably a lot of it. I wanted to predict language, and one corpus of language data that I already knew about from school was the Europarl Dataset . This is a corpus of EU meetings that are translated into the languages...
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