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Found 13 papers, 6 papers with code
Sequential Deformation for Accurate Scene Text Detection
Scene text detection has been significantly advanced over recent years, especially after the emergence of deep neural network.
Exploring RWKV for Memory Efficient and Low Latency Streaming ASR
Recently, self-attention-based transformers and conformers have been introduced as alternatives to RNNs for ASR acoustic modeling.
BAT: Boundary aware transducer for memory-efficient and low-latency ASR
Recently, recurrent neural network transducer (RNN-T) gains increasing popularity due to its natural streaming capability as well as superior performance.
Ranked #9 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
An Empirical Study of Language Model Integration for Transducer based Speech Recognition
Based on the DR method, we propose a low-order density ratio method (LODR) by replacing the estimation with a low-order weak language model.
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR
The simulation module is jointly trained with the ASR model using a self-supervised loss; the ASR model is optimized with the usual ASR loss, e. g., CTC-CRF as used in our experiments.
Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech Recognition: A Comparative Study
In this paper, we systematically compare the performance of three schemes to exploit external single-channel data for multi-channel end-to-end ASR, namely back-end pre-training, data scheduling, and data simulation, under different settings such as the sizes of the single-channel data and the choices of the front-end.
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings
The use of phonological features (PFs) potentially allows language-specific phones to remain linked in training, which is highly desirable for information sharing for multilingual and crosslingual speech recognition methods for low-resourced languages.
Exploiting Single-Channel Speech For Multi-channel End-to-end Speech Recognition
Recently, the end-to-end training approach for neural beamformer-supported multi-channel ASR has shown its effectiveness in multi-channel speech recognition.
Deformable TDNN with adaptive receptive fields for speech recognition
Time Delay Neural Networks (TDNNs) are widely used in both DNN-HMM based hybrid speech recognition systems and recent end-to-end systems.
The SLT 2021 children speech recognition challenge: Open datasets, rules and baselines
Automatic speech recognition (ASR) has been significantly advanced with the use of deep learning and big data.
Sound Audio and Speech Processing
Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients
Using ST gradients to support sub-graph sampling is a core element to achieve efficient NAS beyond DARTS and SNAS.
Ranked #1 on
Speech Recognition
on WSJ dev93
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency
In this paper, we present a new open source toolkit for speech recognition, named CAT (CTC-CRF based ASR Toolkit).
Ranked #1 on
Speech Recognition
on Hub5'00 FISHER-SWBD
CAT: CRF-based ASR Toolkit
In this paper, we present a new open source toolkit for automatic speech recognition (ASR), named CAT (CRF-based ASR Toolkit).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
