216 citations · 521 across the 32 of their papers we have counts for
34 papers
Less Peaky and More Accurate CTC Forced Alignment by Label Priors
Ruizhe Huang, Xiaohui Zhang, Zhaoheng Ni +9
Connectionist temporal classification (CTC) models are known to have peaky output distributions. Such behavior is not a problem for automatic speech recognition (ASR), but it can c…
On Speaker Attribution with SURT
Desh Raj, Matthew Wiesner, Matthew Maciejewski +3
The Streaming Unmixing and Recognition Transducer (SURT) has recently become a popular framework for continuous, streaming, multi-talker speech recognition (ASR). With advances in…
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…
Alternative Pseudo-Labeling for Semi-Supervised Automatic Speech Recognition
Han Zhu, Dongji Gao, Gaofeng Cheng +3
When labeled data is insufficient, semi-supervised learning with the pseudo-labeling technique can significantly improve the performance of automatic speech recognition. However, p…
SURT 2.0: Advances in Transducer-based Multi-talker Speech Recognition
Desh Raj, Daniel Povey, Sanjeev Khudanpur
The Streaming Unmixing and Recognition Transducer (SURT) model was proposed recently as an end-to-end approach for continuous, streaming, multi-talker speech recognition (ASR). Des…
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts
Dongji Gao, Matthew Wiesner, Hainan Xu +3
This paper presents a novel algorithm for building an automatic speech recognition (ASR) model with imperfect training data. Imperfectly transcribed speech is a prevalent issue in…