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Found 6 papers, 4 papers with code
CPPF: A contextual and post-processing-free model for automatic speech recognition
To address this issue, we draw inspiration from the multifaceted capabilities of LLMs and Whisper, and focus on integrating multiple ASR text processing tasks related to speech recognition into the ASR model.
Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization
To reduce the peak latency, we propose a simple and novel method named peak-first regularization, which utilizes a frame-wise knowledge distillation function to force the probability distribution of the CTC model to shift left along the time axis instead of directly modifying the calculation process of CTC loss and gradients.
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.
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
CRF-based Single-stage Acoustic Modeling with CTC Topology
CTC-CRF is conceptually simple, which basically implements a CRF layer on top of features generated by the bottom neural network with the special state topology.
Ranked #2 on
Speech Recognition
on WSJ eval93
