Improving ASR Error Detection with RNNLM Adaptation

Abstract

Applications of automatic speech recognition (ASR) such as broadcast transcription and dialog systems, can be helped by the ability to detect errors in the ASR output. The field of ASR error detection has emerged as a way to detect and subsequently correct ASR errors. The most common approach for ASR error detection is features-based, where a set of features are extracted from the ASR output and used to train a classifier to predict correct/incorrect labels.Language models (LMs), either from the ASR decoder or externally trained, can be used to provide features to an ASR error detection system, through scores computed on the ASR output. Recently, recurrent neural network language models (RNNLMs) features were proposed for ASR error detection with improvements to the classification rate, thanks to their ability to model longer-range context.RNNLM adaptation, through the introduction of auxiliary features that encode domain, has been shown to improve ASR performance. This work investigates whether RNNLM adaptation techniques can also improve ASR error detection performance in the context of multi-genre broadcast ASR. The results show that an overall improvement of about 1% in the F-measure can be achieved using adapted RNNLM features.