6 citations · 16 across the 19 of their papers we have counts for
5 papers · 1 filter
FastInject: Injecting Unpaired Text Data into CTC-based ASR training
Keqi Deng, Philip C. Woodland
Recently, connectionist temporal classification (CTC)-based end-to-end (E2E) automatic speech recognition (ASR) models have achieved impressive results, especially with the develop…
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition
Keqi Deng, Philip C. Woodland
Although end-to-end (E2E) automatic speech recognition (ASR) has shown state-of-the-art recognition accuracy, it tends to be implicitly biased towards the training data distributio…
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…
Label-Synchronous Neural Transducer for End-to-End ASR
Keqi Deng, Philip C. Woodland
Neural transducers provide a natural way of streaming ASR. However, they augment output sequences with blank tokens which leads to challenges for domain adaptation using text data.…
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…