Codebook design for speech guided car infotainment systems

Abstract

In car infotainment 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. Previous approaches did not address the constraint of conserving the main language performance when they extended their systems to cover multilingual input. In this paper we present an approach for speech recognition of multiple languages with constrained resources on embedded devices. Speech recognizers on such systems are typically to-date semi-continuous speech recognizers, which are based on vector quantization. We provide evidence that common vector quantization algorithms are not optimal for such systems when they have to cope with input from multiple languages. Our new method combines information from multiple languages and creates a new codebook that can be used for efficient vector quantization in multilingual scenarios. Experiments show significant improved speech recognition results. © 2008 Springer-Verlag Berlin Heidelberg.