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Found 7 papers, 0 papers with code
Do self-supervised speech and language models extract similar representations as human brain?
Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception.
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition
Self-Supervised Learning (SSL) Automatic Speech Recognition (ASR) models have shown great promise over Supervised Learning (SL) ones in low-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
UFO2: A unified pre-training framework for online and offline speech recognition
In this paper, we propose a Unified pre-training Framework for Online and Offline (UFO2) Automatic Speech Recognition (ASR), which 1) simplifies the two separate training workflows for online and offline modes into one process, and 2) improves the Word Error Rate (WER) performance with limited utterance annotating.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition
End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervision.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Incremental Learning for End-to-End Automatic Speech Recognition
In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintaining the performance on its originally learned ones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Research on Modeling Units of Transformer Transducer for Mandarin Speech Recognition
To improve the performance of RNN-T for Mandarin speech recognition task, a novel transformer transducer with the combination architecture of self-attention transformer and RNN is proposed.
Learning Enhanced Resolution-wise features for Human Pose Estimation
Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps.
