Extending Word-Level Quality Estimation for Post-Editing Assistance

We define a novel concept called extended word alignment in order to improve post-editing assistance efficiency. Based on extended word alignment, we further propose a novel task called refined word-level QE that outputs refined tags and word-level correspondences. Compared to original word-level QE, the new task is able to directly point out editing operations, thus improves efficiency. To extract extended word alignment, we adopt a supervised method based on mBERT. To solve refined word-level QE, we firstly predict original QE tags by training a regression model for sequence tagging based on mBERT and XLM-R. Then, we refine original word tags with extended word alignment. In addition, we extract source-gap correspondences, meanwhile, obtaining gap tags. Experiments on two language pairs show the feasibility of our method and give us inspirations for further improvement.

ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of Kaldi

The availability of open-source software is playing a remarkable role in automatic speech recognition (ASR). Kaldi, for instance, is widely used to develop state-of-the-art offline and online ASR systems. This paper describes the "ExKaldi-RT," online ASR toolkit implemented based on Kaldi and Python language. ExKaldi-RT provides tools for providing a real-time audio stream pipeline, extracting acoustic features, transmitting packets with a remote connection, estimating acoustic probabilities with a neural network, and online decoding. While similar functions are available built on Kaldi, a key feature of ExKaldi-RT is completely working on Python language, which has an easy-to-use interface for online ASR system developers to exploit original research, for example, by applying neural network-based signal processing and acoustic model trained with deep learning frameworks. We performed benchmark experiments on the minimum LibriSpeech corpus, and showed that ExKaldi-RT could achieve competitive ASR performance in real-time.