85 citations · 112 across the 6 of their papers we have counts for
7 papers
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +3
Non-autoregressive transformer models have achieved extremely fast inference speed and comparable performance with autoregressive sequence-to-sequence models in neural machine tran…
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Although attention based end-to-end models have achieved promising performance in speech recognition, the multi-pass forward computation in beam-search increases inference time cos…
Adversarial Transfer Learning for Punctuation Restoration
Jiangyan Yi, Jianhua Tao, Ye Bai +2
Previous studies demonstrate that word embeddings and part-of-speech (POS) tags are helpful for punctuation restoration tasks. However, two drawbacks still exist. One is that word…
Rnn-transducer with language bias for end-to-end Mandarin-English code-switching speech recognition
Shuai Zhang, Jiangyan Yi, Zhengkun Tian +2
Recently, language identity information has been utilized to improve the performance of end-to-end code-switching (CS) speech recognition. However, previous works use an additional…
Synchronous Transformers for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder. The asynchr…
Self-Attention Transducers for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +2
Recurrent neural network transducers (RNN-T) have been successfully applied in end-to-end speech recognition. However, the recurrent structure makes it difficult for parallelizatio…