SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition

Error Detection in Automatic Speech Recognition

We offer a supervised machine learning approach for recognizing erroneous words in the output of a speech recognizer. We have investigated several sets of features combined with two word configurations, and compared the performance of two classifiers: Decision Trees and Naïve Bayes. Evaluation was performed on a corpus of 400 spoken referring expressions, with Decision Trees yielding a high rec...

Recent Improvements on Error Detection for Automatic Speech Recognition

Automatic speech recognition(ASR) offers the ability to access the semantic content present in spoken language within audio and video documents. While acoustic models based on deep neural networks have recently significantly improved the performances of ASR systems, automatic transcriptions still contain errors. Errors perturb the exploitation of these ASR outputs by introducing noise to the te...

Soft margin estimation for automatic speech recognition

Context-based Speech Recognition Error Detection and Correction

In this paper we present preliminary results of a novel unsupervised approach for highprecision detection and correction of errors in the output of automatic speech recognition systems. We model the likely contexts of all words in an ASR system vocabulary by performing a lexical co-occurrence analysis using a large corpus of output from the speech system. We then identify regions in the data th...

Robust Error Correction of Continuous Speech Recognition

We present a post-processing technique for correcting errors committed by an arbitrary continuous speechrecognizer. The technique leverages our observation that consistent recognition errors arising from mismatched training and usageconditions can be modeled and corrected. We have implemented a post-processor called SPEECHPP to correct word-level errors, and we show that this post-processing te...