Abstract

When considering building speech recognition and keyword search (KWS) systems for a ‘new’ language, two key questions are “how much data is going to be needed?” and “what resources are available?”. This talk will look at how to predict the first and how to mitigate for the second if the answer is “limited”. A wide range of factors affect recognition and KWS  performance from one language to the next, such as phone set size, morphological richness and dialect/accent variation. The harder the language the more data that is generally required to achieve the same level. This talk will present analysis of performance across a range of factors and languages, within and across language families, from the IARPA Babel programme. A method to predict performance given a small amount of data from a language will be presented. When data resources are limited, performance can be boosted by exploiting data from other languages. This talk will also discuss the use of multilingual features and multilingual models for such limited resource case.

Biography

Kate Knill is a Senior Research Associate at Engineering Department, Cambridge University, UK, working on automatic spoken language teaching and assessment within the ALTA Institute. She previously worked on the rapid development of speech systems for new languages on the IARPA BABEL project. She holds a PhD in Digital Signal Processing from Imperial College, London University, UK. Kate has over 25 years experience in speech and language processing in industry and academia, including leading the development of over 20 languages as Languages Manager at Nuance Communications (2000-2002) and establishing and leading the Speech Technology Group, Toshiba Cambridge Research Lab, UK (2002-2012). She was a member of the IEEE SLTC 2009-2012, is an ISCA Board member (2013-2021) and is currently Secretary of ISCA.