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Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models
Rui Zhao, Jian Xue, Partha Parthasarathy +2
Neural transducer is now the most popular end-to-end model for speech recognition, due to its naturally streaming ability. However, it is challenging to adapt it with text-only dat…
Speech separation with large-scale self-supervised learning
Zhuo Chen, Naoyuki Kanda, Jian Wu +6
Self-supervised learning (SSL) methods such as WavLM have shown promising speech separation (SS) results in small-scale simulation-based experiments. In this work, we extend the ex…
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer
Xun Gong, Yu Wu, Jinyu Li +4
Traditional automatic speech recognition~(ASR) systems usually focus on individual utterances, without considering long-form speech with useful historical information, which is mor…
Simulating realistic speech overlaps improves multi-talker ASR
Muqiao Yang, Naoyuki Kanda, Xiaofei Wang +5
Multi-talker automatic speech recognition (ASR) has been studied to generate transcriptions of natural conversation including overlapping speech of multiple speakers. Due to the di…
Self-supervised learning with bi-label masked speech prediction for streaming multi-talker speech recognition
Zili Huang, Zhuo Chen, Naoyuki Kanda +6
Self-supervised learning (SSL), which utilizes the input data itself for representation learning, has achieved state-of-the-art results for various downstream speech tasks. However…
Streaming, fast and accurate on-device Inverse Text Normalization for Automatic Speech Recognition
Yashesh Gaur, Nick Kibre, Jian Xue +5
Automatic Speech Recognition (ASR) systems typically yield output in lexical form. However, humans prefer a written form output. To bridge this gap, ASR systems usually employ Inve…