6 citations · 8 across the 3 of their papers we have counts for
5 papers
Label-Synchronous Neural Transducer for E2E Simultaneous Speech Translation
Keqi Deng, Philip C. Woodland
While the neural transducer is popular for online speech recognition, simultaneous speech translation (SST) requires both streaming and re-ordering capabilities. This paper present…
Decoupled Structure for Improved Adaptability of End-to-End Models
Keqi Deng, Philip C. Woodland
Although end-to-end (E2E) trainable automatic speech recognition (ASR) has shown great success by jointly learning acoustic and linguistic information, it still suffers from the ef…
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax
Keqi Deng, Philip C. Woodland
End-to-end (E2E) automatic speech recognition (ASR) implicitly learns the token sequence distribution of paired audio-transcript training data. However, it still suffers from domai…
Improving Streaming End-to-End ASR on Transformer-based Causal Models with Encoder States Revision Strategies
Zehan Li, Haoran Miao, Keqi Deng +4
There is often a trade-off between performance and latency in streaming automatic speech recognition (ASR). Traditional methods such as look-ahead and chunk-based methods, usually…
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model
Keqi Deng, Songjun Cao, Yike Zhang +1
Recently, self-supervised pretraining has achieved impressive results in end-to-end (E2E) automatic speech recognition (ASR). However, the dominant sequence-to-sequence (S2S) E2E m…