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20192025
most citedLingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

184 citations · 202 across the 10 of their papers we have counts for

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Showing eess.ASShow all

5 papers · 1 filter

eess.AS20221 cited

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…

eess.AS2022

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…

eess.AS2021

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…

eess.AS2021

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…

eess.AS202112 cited

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…