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Found 7 papers, 1 papers with code
Contextualization of ASR with LLM using phonetic retrieval-based augmentation
Large language models (LLMs) have shown superb capability of modeling multimodal signals including audio and text, allowing the model to generate spoken or textual response given a speech input.
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models
This paper introduces a novel training framework called Focused Discriminative Training (FDT) to further improve streaming word-piece end-to-end (E2E) automatic speech recognition (ASR) models trained using either CTC or an interpolation of CTC and attention-based encoder-decoder (AED) loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Enhancing CTC-based speech recognition with diverse modeling units
In recent years, the evolution of end-to-end (E2E) automatic speech recognition (ASR) models has been remarkable, largely due to advances in deep learning architectures like transformer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Treatise On FST Lattice Based MMI Training
Maximum mutual information (MMI) has become one of the two de facto methods for sequence-level training of speech recognition acoustic models.
AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale
For research community, we hope that AISHELL-2 corpus can be a solid resource for topics like transfer learning and robust ASR.
AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline
An open-source Mandarin speech corpus called AISHELL-1 is released.
Purely sequence-trained neural networks for ASR based on lattice-free MMI
Models trained with LFMMI provide a relative word error rate reduction of ∼11. 5%, over those trained with cross-entropy objective function, and ∼8%, over those trained with cross-entropy and sMBR objective functions.
Ranked #5 on
Speech Recognition
on WSJ eval92
