Jennifer Williams

PhD Candidate

Centre for Speech Technology Research (CSTR)

The University of Edinburgh, Scotland, UK

Email: j.williams@ed.ac.uk

2021 Short CV

(full CV available upon request)

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About Me

I am a PhD candidate in the Centre for Speech Technology Research (CSTR). I am supervised by Simon King (primary), Steve Renals, and Junichi Yamagishi. My work explores deep learning approaches to model and control various factors of the speech signal including self-supervised learning and disentanglement. I am also interested in ethical issues surrounding speech technology, including: voice data privacy, voice spoofing/anti-spoofing, and speech technology security.

From February 2020 to February 2021, I was a visiting student in the Yamagishi Lab at the National Institute of Informatics (NII) in Tokyo, Japan. During that time, I collaborated with other PhD students and post-docs to develop techniques that disentangle information in the speech signal for use in speech synthesis.

In 2019, I led a team in the Automatic Speaker Verification ASVspoof Challenge 2019. We submitted two system entries, one for each of the countermeasures tasks: 1) logical access task (text-to-speech, voice conversion, and copy-synthesis), and 2) physical access task (speech intercept and replay under various conditions). In July 2018, I worked on a team that earned First Place for multi-modal emotion recognition in the 2018 ACL Multimodal Challenge using audio, text, and video data collected 'in the wild'. In December 2017, I participated in the Alan Turing Institute Data Study Group (DSG), and worked on a team developing natural language processing analytics and insights from ISIS extremist propaganda for the UK Cabinet Office Defence and Security.

Before starting my PhD, I spent 5 years on the technical staff at MIT Lincoln Laboratory, in the Human Language Technology Group (now the AI Technology and Systems Group). I had the pleasure to work on a variety of research projects which involved rapid prototyping solutions for speech and text data. Prior to joining MIT, I was a visiting scholar at Institute for Infocomm Research (I2R) in Singapore where I worked on simultaneous translation. I earned my Bachelors degree in Applied Linguistics at Portland State University ('09), and my Masters degree in Computational Linguistics at Georgetown University ('12). I have held numerous STEM internships in industry fields such as physics, engineering, and computer science - as well as additional coursework.

My PhD is funded by a generous studentship that is administered by the UoE School of Informatics Centre for Doctoral Training (CDT) in Data Science, and sponsored by the UK Engineering and Physical Sciences Research Council (EPSRC)

Selected Publications

Recent PhD Experience

[In the UK system: Tutor = lead groups of 10-20 students through sample problems and research; Lab Demonstrator = nurture student curiosity about computer programming in the lab; Marker = grade student coursework, project reports, and/or exams; TA = develop course material and hold office hours]

What Else is New?

  • 2019 Interspeech paper on Style Factors named as a 'Top 10 Highlights of Interspeech' by Cogito Corporation
  • 2019 UK Speech Conference in Birmingham, UK
  • 2018 ACL First Place Emotion Recognition, Multimodal Challenge
  • 2018 UK Speech Conference in Dublin, Ireland
  • 2018 COLING Outstanding Mentor Award
  • 2017 MIT Hackathon: Best Innovation Award
  • 2016 MIT News: Cross Language Information Retrieval
  • 2015 University of Massachusetts-Amherst Career Talk
  • For Fun

    One of my first fascinations with sound came from a summer job that I held as an undergraduate student. I had spent a summer presenting physics experiments to the public at the Oregon Museum of Science and Industry (Portland, Oregon). I had the pleasure of explaining a variety of interesting concepts - one of them was nodal patterns made by sound waves as they travelled through a medium known as a Chladni Plate. Different sound frequencies will produce different nodal patterns, with higher frequency sounds producing more complex patterns.

    I also enjoy thinking in the space of philosophy of the mind, including The Extended Mind. My earliest endeavors in linguistics, machine learning, and artificial intelligence were inspired by the work of Andy Clark (Edinburgh) and Hilary Putnam (Harvard) - each in different ways.

    In my free time I work on an interesting problem in number theory.

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