Benjamin Etienne is a data scientist at Rogervoice, a mobile app that allows deaf and hard-of-hearing people to use the telephone. Ben shares his inspirational story about how he taught himself data science and machine learning in the evenings, so he could work in a more technical role. He tells us why he’s not keen on Kaggle competitions, and why getting a job in data science is the best way to master it.
Ben introduces us to the challenges faced by the deaf and hard-of-hearing community, and how they can overcome them with the help of voice technology. We cover how Rogervoice works from both functional and technical standpoints, and discuss the pros and cons of using a commercial cloud-based speech API versus developing a custom in-house speech-to-text system. Ben reveals his reasoning behind his choice of machine learning models, and describes the advantages of using connectionist temporal classification (CTC).
We then discuss the state of data science today, the limitations of current models and data preprocessing techniques, and how an understanding of the underlying psychology and neurology of users can help us design more effective voice technologies.
Links from this episode:
- Rogervoice: https://www.rogervoice.com
- CTC intro: https://distill.pub/2017/ctc/
- CTC article on Medium: https://gab41.lab41.org/speech-recognition-you-down-with-ctc-8d3b558943f0
- …more show notes on the Website : http://bit.ly/voicetechpodcast
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