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[PDF][PDF] Recent advances in end-to-end automatic speech recognition
J Li - APSIPA Transactions on Signal and Information …, 2022 - nowpublishers.com
Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …
End-to-end speech recognition: A survey
In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …
learning has brought considerable reductions in word error rate of more than 50% relative …
Context-aware transformer transducer for speech recognition
End-to-end (E2E) automatic speech recognition (ASR) systems often have difficulty
recognizing uncommon words, that appear infrequently in the training data. One promising …
recognizing uncommon words, that appear infrequently in the training data. One promising …
Contextual adapters for personalized speech recognition in neural transducers
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR)
models is a challenge due to the lack of training data. A standard way to address this issue …
models is a challenge due to the lack of training data. A standard way to address this issue …
Contextual RNN-T for open domain ASR
M Jain, G Keren, J Mahadeokar, G Zweig… - ar** for contextual biasing in end-to-end ASR
In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid interest in the
speech recognition community. They convert speech input to text units in a single trainable …
speech recognition community. They convert speech input to text units in a single trainable …