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Found 10 papers, 5 papers with code
SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability
It possesses the advantages of AED-based model's accuracy, NAR model's efficiency, and explicit customization capacity of superior performance.
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)
FunASR: A Fundamental End-to-End Speech Recognition Toolkit
FunASR offers models trained on large-scale industrial corpora and the ability to deploy them in applications.
Ranked #1 on
Speech Recognition
on WenetSpeech
(using extra training data)
Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System
Estimating confidence scores for recognition results is a classic task in ASR field and of vital importance for kinds of downstream tasks and training strategies.
Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model
Conventional ASR systems use frame-level phoneme posterior to conduct force-alignment~(FA) and provide timestamps, while end-to-end ASR systems especially AED based ones are short of such ability.
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding
We also demonstrate the feasibility of a more efficient training strategy.
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
Deep learning models are the state-of-the-art methods for semantic point cloud segmentation, the success of which relies on the availability of large-scale annotated datasets.
Cascade RNN-Transducer: Syllable Based Streaming On-device Mandarin Speech Recognition with a Syllable-to-Character Converter
End-to-end models are favored in automatic speech recognition (ASR) because of its simplified system structure and superior performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling
Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and possibly incomplete, which restricts its use for a high-quality surface recovery.
The ASRU 2019 Mandarin-English Code-Switching Speech Recognition Challenge: Open Datasets, Tracks, Methods and Results
The paper then presents an overview of the results and system performance in the three tracks.
