Multilingual weighted codebooks for non-native speech recognition

Abstract

In many embedded systems commands and other words in the user's main language must be recognized with maximum accuracy, but it should be possible to use foreign names as they frequently occur in music titles or city names. Example systems with constrained resources are navigation systems, mobile phones and MP3 players. Speech recognizers on embedded systems are typically semi-continuous speech recognizers based on vector quantization. Recently we introduced Multilingual Weighted Codebooks (MWCs) for such systems. Our previous work shows significant improvements for the recognition of multiple native languages. However, open questions remained regarding the performance on non-native speech. We evaluate on four different non-native accents of English, and our MWCs produce always significantly better results than a native English codebook. Our best result is a 4.4% absolute word accuracy improvement. Further experiments with non-native accented speech give interesting insights in the attributes of non-native speech in general. © 2008 Springer-Verlag Berlin Heidelberg.