Joint language models for automatic speech recognition and understanding

Abstract

Language models (LMs) are one of the main knowledge sources used by automatic speech recognition (ASR) and Spoken Language Understanding (SLU) systems. In ASR systems they are optimized to decode words from speech for a transcription task. In SLU systems they are optimized to map words into concept constructs or interpretation representations. Performance optimization is generally designed independently for ASR and SLU models in terms of word accuracy and concept accuracy respectively. However, the best word accuracy performance does not always yield the best understanding performance. In this paper we investigate how LMs originally trained to maximize word accuracy can be parametrized to account for speech understanding constraints and maximize concept accuracy. Incremental reduction in concept error rate is observed when a LM is trained on word-to-concept mappings. We show how to optimize the joint transcription and understanding task performance in the lexical-semantic relation space.