6 accepted papers
Full-text error correction with Large Language Models (LLMs) for Automatic Speech Recognition (ASR) is attracting increased attention for its ability to address a wide range of error types, such as punctuation restoration and inverse text normalization, across long context. However, challenges remai…
Automatic speech recognition (ASR) has suffered great performance degradation when facing low-resource languages with limited training data. In this work, we propose a series of training strategies to exploring more effective data utilization for low-resource speech recognition. In low-resource scen…
To improve automatic speech recognition, increasing work has attempted to further fix the output of ASR systems with advanced sequence models. However, the output of ASR systems differs significantly from the input form of standard sequence models. To encompass richer information, the output of ASR…
Punctuation prediction is essential for automatic speech recognition (ASR). Although many works have been proposed for punctuation prediction, the on-device scenarios are rarely discussed with an end-to-end ASR. The punctuation prediction task is often treated as a post-processing of ASR outputs, bu…
This paper describes the SJTU system for ICASSP Multi-modal Information based Speech Processing Challenge (MISP) 2021. To solve the speech recognition problem in real complex environments where time-synchronized near- and far-field signals are available for training an enhancement frontend. We build…
Although great progress has been made on automatic speech recognition (ASR) systems, children’s speech recognition still remains a challenging task. General ASR systems for children’s speech suffer from the lack of corpora and mismatch between children’s and adults’ speech. Efforts have been made to…