Search Results for author:
Found 5 papers, 0 papers with code
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
A key component of building safe and reliable language models is enabling the models to appropriately refuse to follow certain instructions or answer certain questions.
Retrieval Augmented Correction of Named Entity Speech Recognition Errors
In recent years, end-to-end automatic speech recognition (ASR) systems have proven themselves remarkably accurate and performant, but these systems still have a significant error rate for entity names which appear infrequently in their training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Exploring Retraining-Free Speech Recognition for Intra-sentential Code-Switching
To achieve such an ambitious goal, new mechanisms for foreign pronunciation generation and language model (LM) enrichment have been devised.
Frame-level SpecAugment for Deep Convolutional Neural Networks in Hybrid ASR Systems
We demonstrate that f-SpecAugment is more effective than the utterance level SpecAugment for deep CNN based hybrid models.
SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition
The most popular way to train very deep CNNs is to use shortcut connections (SC) together with batch normalization (BN).
