Voice and Language Tech // Catherin Breslin // Coffee Sessions #129
MLOps.community
How Did You Become Into Machine Learning?
I've always been interested in massing computers, physics, and all of that. I ended up studying engineering and computer science as joint honors. So this is how I started on the path to machine learning. And from there, I've spent my career in different aspects of voice and language technology.
MLOps Coffee Sessions #129 {Podcast BTS} with Catherin Breslin, Voice and Language Tech co-hosted by Adam Sroka.
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// Abstract
Back in the day, Speech Recognition was its own thing. It's a very different flavor of Data Science. You could not use a lot of the tools. It wouldn't cross over to this type of machine learning.
Now, with the advancements, Speech Recognition and Machine learning are coming in together. It's interesting to hear right from someone with a Ph.D. level working with some of the biggest companies in the world doing it. The fact that something like Alexa is lots of models back to back and just fathom the complexity of that is quite cool!
// Bio
Catherine is a machine learning scientist and consultant based in Cambridge UK, and the founder of Kingfisher Labs consulting. Since completing her Ph.D. at the University of Cambridge in 2008, Catherine has commercial and academic experience in automatic speech recognition, natural language understanding, and human-computer dialogue systems, having previously worked at Cambridge University, Toshiba Research, Amazon Alexa, and Cobalt Speech. Catherine has been excited by the application of research to real-world problems involving speech and language at scale.
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// Related Links
www.catherinebreslin.co.uk
https://catherinebreslin.medium.com/
MLOps Community Newsletter: https://airtable.com/shrx9X19pGTWa7U3YTwitter: https://twitter.com/catherinebuk
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Timestamps:
[00:00] Catherine's preferred coffee
[01:50] Takeaways
[03:59] Introduction to Catherine Breslin
[05:04] Subscribe to our newsletter!
[06:25] Catherine's background
[08:13] Speech Recognition trajectory
[09:36] Challenges around technologies and tools
[11:34] Reflective trend
[13:02] Developer experiences hiccups
[15:09] Speech Recognition use case backup
[16:56] Toshiba research
[17:48] Transition from a research lab to working in the industry
[20:01] Unit test of Speech Recognition
[20:56] Alexa
[22:33] Maturity process of Speech Recognition
[26:48] Speech Recognition unrecognizing challenges
[30:38] Mechanical Terk
[33:00] Social media listening
[36:48] Development of Speech Recognition, excited about
[37:23] Data from people for the Speech Recognition system vs Scowering news vs watching YouTube for a long time
[40:00] Disappearing Languages
[43:17] Speech-to-speech translation
[44:04] Interesting ways to use unfamiliar models to achieve a result
[45:40] Meeting transcriptions
[48:37] First toy problem of a new Speech Recognition learner
[51:37] Kingfisher Labs' problems to tackle
[53:38] Translation layer
[54:15] Connect with Catherine on Twitter and LinkedIn for available jobs
[54:43] Wrap up
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