Speech recognition error correction using maximum entropy language model

Minwoo Jeong, Sangkeun Jung, Gary Geunbae Lee

Abstract

A speech interface is often required in many application environments, such as telephone-based information retrieval, car navigation systems, and user-friendly interfaces, but the low speech recognition rate makes it difficult to extend its application to new fields. We propose a domain adaptation technique via error correction with a maximum entropy language model, which is a general and elegant framework to combine higher level linguistic knowledge. Our approach has the ability to correct both semantic and lexical errors in 1-best output from the black-box style speech recognizer, and can improve the performance of speech recognition and application system. Through extensive experiments using a speechdriven in-vehicle telematics information retrieval and spoken language understanding, we demonstrate the superior performance of our approach and some advantages over previous lexical-oriented error correction approaches.