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Found 7 papers, 1 papers with code
Effective internal language model training and fusion for factorized transducer model
However, even with the adoption of factorized transducer models, limited improvement has been observed compared to shallow fusion.
Correction Focused Language Model Training for Speech Recognition
Language models (LMs) have been commonly adopted to boost the performance of automatic speech recognition (ASR) particularly in domain adaptation tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Recovering from Privacy-Preserving Masking with Large Language Models
To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data.
Contextual Biasing of Named-Entities with Large Language Models
We propose to leverage prompts for a LLM without fine tuning during rescoring which incorporate a biasing list and few-shot examples to serve as additional information when calculating the score for the hypothesis.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Adaptive Multi-Corpora Language Model Training for Speech Recognition
Thus, the data sampling strategy is important to the adaptation performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Proximal Mapping for Deep Regularization
Underpinning the success of deep learning is effective regularizations that allow a variety of priors in data to be modeled.
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Invariance (defined in a general sense) has been one of the most effective priors for representation learning.
