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Found 5 papers, 0 papers with code
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
Language Agnostic Data-Driven Inverse Text Normalization
With the emergence of automatic speech recognition (ASR) models, converting the spoken form text (from ASR) to the written form is in urgent need.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Data Driven Inverse Text Normalization using Data Augmentation
Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving N-gram Language Models with Pre-trained Deep Transformer
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference.
