Jiangyan Yi

Self-Attention Transducers for End-to-End Speech Recognition

2019

Recurrent neural network transducers (RNN-T) have been successfully applied in end-to-end speech recognition. However, the recurrent structure makes it difficult for parallelization . In this paper, we propose a self-attention transducer (SA-T) for speech recognition. …

Distilling Knowledge for Distant Speech Recognition via Parallel Data

2019

In order to improve the performance of distant speech recognition tasks, this paper proposes to distill knowledge from the close-talking model to the distant model using parallel data. The close-talking model is called the teacher …

One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition

2020 · arXiv (Cornell University)

The RNN-Transducers and improved attention-based encoder-decoder models are widely applied to streaming speech recognition. Compared with these two end-to-end models, the CTC model is more efficient in training and inference. However, it cannot capture the …

Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition

2020 · arXiv (Cornell University)

Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models' performance. In this paper, we propose a decoupled transformer model …

TST: Time-Sparse Transducer for Automatic Speech Recognition

2023 · arXiv (Cornell University)

End-to-end model, especially Recurrent Neural Network Transducer (RNN-T), has achieved great success in speech recognition. However, transducer requires a great memory footprint and computing time when processing a long decoding sequence. To solve this problem, …