Abstract

Abstract: In this talk I will present a method for training an automatic speech recognition system for a language for which absolutely no transcribed training data is available – a so-called “zero resource” scenario. Our approach is inspired by traditional methods of decipherment used in code-breaking, where strong prior knowledge of the language in question can be leveraged to crack a code with only limited quantities of cipher text. When applying this technqiue to speech, we use cross-lingual knowledge transfer as a means of making the problem tractable, and find that new languages can be “deciphered” with as little as 20 minutes of audio, and no phonetic knowledge of the language in question.

Bio: Peter Bell is a Reader in Speech Technology in the School of Informatics at the University of Edinburgh, and a member of the Centre for Speech Technology Research. His research interests include all aspects of automatic speech recognition development for low-resource languages. He was previously a co-investigator on the IARPA MATERIAL project and is currently principal investigator on the Unmute project, which aims to develop spoken language interfaces for traditionally marginalised language communities.