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Acoustic Word Embeddings for Low Resource Speech Processing with Herman Kamper - TWiML Talk #191

Acoustic Word Embeddings for Low Resource Speech Processing with Herman Kamper - TWiML Talk #191

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)


Acoustic Word Embeddings for Low Resource Speech Processing with Herman Kamper - TWiML Talk #191

FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

ratings:
Length:
61 minutes
Released:
Oct 16, 2018
Format:
Podcast episode

Description

In this episode of our Deep Learning Indaba Series, we’re joined by Herman Kamper, Lecturer in the electrical and electronics engineering department at Stellenbosch University in SA and a co-organizer of the Indaba. Herman and I discuss his work on limited- and zero-resource speech recognition, how those differ from regular speech recognition, and the tension between linguistic and statistical methods in this space. We dive into the specifics of the methods being used and developed in Herman’s lab as well, including how phoneme data is used for segmenting and processing speech data. The full show notes for this episode can be found at https://twimlai.com/talk/191. For more on the Deep Learning Indaba series, visit https://twimlai.com/indaba2018.  
Released:
Oct 16, 2018
Format:
Podcast episode

Titles in the series (100)

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.