Anton Mitrofanov, Mariya Korenevskaya, Ivan Podluzhny, Yuri Khokhlov, Aleksandr Laptev, Andrei Andrusenko, Aleksei Ilin, Maxim Korenevsky, Ivan Medennikov, Aleksei Romanenko
Neural network-based language models are commonly used in rescoring approaches to improve the quality of modern automatic speech recognition (ASR) systems. Most of the existing methods are computationally expensive since they use autoregressive language models. We propose a novel rescoring approach, which processes the entire lattice in a single call to the model. The key feature of our rescoring policy is a novel non-autoregressive Lattice Transformer Language Model (LT-LM). This model takes the whole lattice as an input and predicts a new language score for each arc. Additionally, we propose the artificial lattices generation approach to incorporate a large amount of text data in the LT-LM training process. Our single-shot rescoring performs orders of magnitude faster than other rescoring methods in our experiments. It is more than 300 times faster than pruned RNNLM lattice rescoring and N-best rescoring while slightly inferior in terms of WER.
ProGRes: Prompted Generative Rescoring on ASR n-Best
McGill University（麦吉尔大学）；Mila-Quebéc AI Institute（米拉-魁北克人工智能研究所）；Concordia University（康考迪亚大学）；Université de Montréal（蒙特利尔大学）；Avignon Université（阿维尼翁大学）
提出 ProGRes 零样本 ASR n-best 重评分方法，融合置信度、LLM 序列评分与提示生成假设，在多识别器上实现 5%–25% 的 WER 相对改善。
MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems
Princeton University, Princeton NJ 08544, USA, , WWW home page: http://users/ iekeland/web/welcome.html Universit\' e de Paris-Sud, Laboratoire d'Analyse Num\' e rique, B\ a timent 425, F-91405 Orsay Cedex, France；Megatronix, Beijing, China Baidu, Inc., Beijing, China Shumei AI Research Institute, Beijing China Newcastle University, Newcastle Upon Tyne, United Kingdom
School of Data Science（数据科学学院）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Fuxi AI Lab, NetEase Inc.（网易伏羲人工智能实验室）
