Search Results for author:
Found 12 papers, 1 papers with code
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics
We explore a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia's Neutral Point of View (NPOV) principle: acknowledge the absence of a single true answer and surface multiple perspectives.
Parameter Efficient Tuning Allows Scalable Personalization of LLMs for Text Entry: A Case Study on Abbreviation Expansion
Abbreviation expansion is a strategy used to speed up communication by limiting the amount of typing and using a language model to suggest expansions.
Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments
A pilot study with 19 non-AAC participants typing on a mobile device by hand demonstrated gains in motor savings in line with the offline simulation, while introducing relatively small effects on overall typing speed.
Towards Agile Text Classifiers for Everyone
Text-based safety classifiers are widely used for content moderation and increasingly to tune generative language model behavior - a topic of growing concern for the safety of digital assistants and chatbots.
An analysis of degenerating speech due to progressive dysarthria on ASR performance
Although personalized automatic speech recognition (ASR) models have recently been designed to recognize even severely impaired speech, model performance may degrade over time for persons with degenerating speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Assessing ASR Model Quality on Disordered Speech using BERTScore
BERTScore was found to be more correlated with human assessment of error type and assessment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Context-Aware Abbreviation Expansion Using Large Language Models
Motivated by the need for accelerating text entry in augmentative and alternative communication (AAC) for people with severe motor impairments, we propose a paradigm in which phrases are abbreviated aggressively as primarily word-initial letters.
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets
Word error rate (WER) thresholds were selected to determine Success Percentage (the percentage of personalized models reaching the target WER) in different application scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech
We demonstrate this on two speech adaptation tasks (atypical and accented speech) and for two state-of-the-art ASR architectures.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment.
On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech
While current state-of-the-art Automatic Speech Recognition (ASR) systems achieve high accuracy on typical speech, they suffer from significant performance degradation on disordered speech and other atypical speech patterns.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
