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Speech Recognition On Wsj Eval92

评估指标

Word Error Rate (WER)

评测结果

各个模型在此基准测试上的表现结果

模型名称	Word Error Rate (WER)	Paper Title	Repository
End-to-end LF-MMI	3.0	End-to-end speech recognition using lattice-free MMI	-
Espresso	3.4	Espresso: A Fast End-to-end Neural Speech Recognition Toolkit
ConformerXXL-P	1.3	BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition	-
CNN over RAW speech (wav)	5.6	-	-
tdnn + chain	2.32	Purely sequence-trained neural networks for ASR based on lattice-free MMI	-
CTC-CRF ST-NAS	2.77	Efficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients	-
Jasper 10x3	6.9	Jasper: An End-to-End Convolutional Neural Acoustic Model
TC-DNN-BLSTM-DNN	3.5	Deep Recurrent Neural Networks for Acoustic Modelling	-
Speechstew 100M	1.3	SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network	-
test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*	3.6	-	-
Convolutional Speech Recognition	3.5	Fully Convolutional Speech Recognition	-
CTC-CRF VGG-BLSTM	3.2	CAT: A CTC-CRF based ASR Toolkit Bridging the Hybrid and the End-to-end Approaches towards Data Efficiency and Low Latency
CTC-CRF 4gram-LM	3.79	CRF-based Single-stage Acoustic Modeling with CTC Topology
Transformer with Relaxed Attention	3.19	Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech Recognition
Deep Speech 2	3.60	Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
Task activating prompting generative correction	2.11	Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting	-
RobustGER	2.2	It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition

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