14 citations · 41 across the 15 of their papers we have counts for
5 papers · 1 filter
Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation
Nithin Rao Koluguri, Travis Bartley, Hainan Xu +4
This paper presents a new method for training sequence-to-sequence models for speech recognition and translation tasks. Instead of the traditional approach of training models on sh…
Label-Looping: Highly Efficient Decoding for Transducers
Vladimir Bataev, Hainan Xu, Daniel Galvez +2
This paper introduces a highly efficient greedy decoding algorithm for Transducer-based speech recognition models. We redesign the standard nested-loop design for RNN-T decoding, s…
Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition
Dongji Gao, Hainan Xu, Desh Raj +3
Training automatic speech recognition (ASR) systems requires large amounts of well-curated paired data. However, human annotators usually perform "non-verbatim" transcription, whic…
Efficient Sequence Transduction by Jointly Predicting Tokens and Durations
Hainan Xu, Fei Jia, Somshubra Majumdar +3
This paper introduces a novel Token-and-Duration Transducer (TDT) architecture for sequence-to-sequence tasks. TDT extends conventional RNN-Transducer architectures by jointly pred…
Multi-blank Transducers for Speech Recognition
Hainan Xu, Fei Jia, Somshubra Majumdar +2
This paper proposes a modification to RNN-Transducer (RNN-T) models for automatic speech recognition (ASR). In standard RNN-T, the emission of a blank symbol consumes exactly one i…