10 citations · 34 across the 16 of their papers we have counts for
17 papers · 1 filter
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…
Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation
Liyong Guo, Xiaoyu Yang, Quandong Wang +9
Knowledge distillation(KD) is a common approach to improve model performance in automatic speech recognition (ASR), where a student model is trained to imitate the output behaviour…
Delay-penalized transducer for low-latency streaming ASR
Wei Kang, Zengwei Yao, Fangjun Kuang +5
In streaming automatic speech recognition (ASR), it is desirable to reduce latency as much as possible while having minimum impact on recognition accuracy. Although a few existing…