184 citations · 203 across the 11 of their papers we have counts for
11 papers
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey
Julia Romberg, Christopher Schröder, Julius Gonsior +2
Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an ite…
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics
Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann +4
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 ans…
Towards Agile Text Classifiers for Everyone
Maximilian Mozes, Jessica Hoffmann, Katrin Tomanek +5
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 digi…
An analysis of degenerating speech due to progressive dysarthria on ASR performance
Katrin Tomanek, Katie Seaver, Pan-Pan Jiang +3
Although personalized automatic speech recognition (ASR) models have recently been designed to recognize even severely impaired speech, model performance may degrade over time for…
Assessing ASR Model Quality on Disordered Speech using BERTScore
Jimmy Tobin, Qisheng Li, Subhashini Venugopalan +3
Word Error Rate (WER) is the primary metric used to assess automatic speech recognition (ASR) model quality. It has been shown that ASR models tend to have much higher WER on speak…
Context-Aware Abbreviation Expansion Using Large Language Models
Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek +3
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 phras…
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets
Jimmy Tobin, Katrin Tomanek
This study investigates the performance of personalized automatic speech recognition (ASR) for recognizing disordered speech using small amounts of per-speaker adaptation data. We…
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech
Katrin Tomanek, Vicky Zayats, Dirk Padfield +2
Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns. Unfortunately, these systems perform poorly when tested on…
Comparing Supervised Models And Learned Speech Representations For Classifying Intelligibility Of Disordered Speech On Selected Phrases
Subhashini Venugopalan, Joel Shor, Manoj Plakal +4
Automatic classification of disordered speech can provide an objective tool for identifying the presence and severity of speech impairment. Classification approaches can also help…
On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech
Katrin Tomanek, Françoise Beaufays, Julie Cattiau +2
While current state-of-the-art Automatic Speech Recognition (ASR) systems achieve high accuracy on typical speech, they suffer from significant performance degradation on disordere…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…