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Found 11 papers, 1 papers with code
FastInject: Injecting Unpaired Text Data into CTC-based ASR training
Recently, connectionist temporal classification (CTC)-based end-to-end (E2E) automatic speech recognition (ASR) models have achieved impressive results, especially with the development of self-supervised learning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Label-Synchronous Neural Transducer for Adaptable Online E2E Speech Recognition
An Auto-regressive Integrate-and-Fire (AIF) mechanism is proposed to generate the label-level encoder representation while retaining low latency operation that can be used for streaming.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Decoupled Structure for Improved Adaptability of End-to-End Models
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 effect of domain shifts, thus limiting potential applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Label-Synchronous Neural Transducer for End-to-End ASR
Hence blank tokens are no longer needed and the prediction network can be easily adapted using text data.
Adaptable End-to-End ASR Models using Replaceable Internal LMs and Residual Softmax
End-to-end (E2E) automatic speech recognition (ASR) implicitly learns the token sequence distribution of paired audio-transcript training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving Streaming End-to-End ASR on Transformer-based Causal Models with Encoder States Revision Strategies
Firstly, we introduce a real-time encoder states revision strategy to modify previous states.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation
Although Transformers have gained success in several speech processing tasks like spoken language understanding (SLU) and speech translation (ST), achieving online processing while keeping competitive performance is still essential for real-world interaction.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving CTC-based speech recognition via knowledge transferring from pre-trained language models
Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2vec2. 0 models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models
The proposed NAR model significantly surpasses previous NAR systems on the AISHELL-1 benchmark and shows a potential for English tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model
In our framework, the encoder is initialized with a pretrained AM (wav2vec2. 0).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning
For the former task, a standard deviation constraint loss (SDC-loss) based end-to-end (E2E) architecture is proposed to identify accents under the same language.
