13th Annual Conference of the International Speech Communication Association

Portland, OR, USA
September 9-13, 2012

Knowledge-Based Word Lattice Rescoring in a Dynamic Context

Todd Shore (1), Friedrich Faubel (1), Hartmut Helmke (2), Dietrich Klakow (1)

(1) Spoken Language Systems, Saarland University, Saarbrücken, Germany
(2) Institute for Flight Guidance, German Aerospace Center (DLR), Braunschweig, Germany

Recent advances in automatic speech recognition (ASR) technology continue to be based heavily on data-driven methods, meaning that the full benefits of such research are often not enjoyed in domains for which there is little training data. Moreover, tractability is often an issue with these methods when conditioning for long-distance dependencies, entailing that many higher-level knowledge sources such as situational knowledge cannot be easily utilized in classification. This paper describes an effort to circumvent this problem by using dynamic contextual knowledge to rescore ASR lattice output using a dynamic weighted constraint satisfaction function. With this method, it was possible to achieve a roughly 80% reduction in WER for ASR in the context of an air traffic control scenario.

Full Paper

Bibliographic reference.  Shore, Todd / Faubel, Friedrich / Helmke, Hartmut / Klakow, Dietrich (2012): "Knowledge-based word lattice rescoring in a dynamic context", In INTERSPEECH-2012, 1083-1086.