Contextual partitioning for speech recognition

Abstract

Many multicore computers are single-user devices, creating the potential to partition by situational usage contexts, similar to how the human brain is organized. Contextual partitioning (CP) permits multiple simplified versions of the same task to exist in parallel, with selection tied to the context in use. We introduce CP for speech recognition, specifically targeted at user interfaces in handheld embedded devices. Contexts are drawn from webpage interactions. CP results in 61% fewer decoding errors, 97% less training for vocabulary changes, near-linear scaling potential with increasing core counts, and up to a potential 90% reduction in power usage.