Adaptation of an EMG-based speech recognizer via meta-learning

Krsto Prorokovic, Michael Wand, Tanja Schultz, Jurgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In nonacoustic speech recognition based on electromyography, i.e. on electrical muscle activity captured by noninvasive surface electrodes, differences between recording sessions are known to cause deteriorating system accuracy. Efficient adaptation of an existing system to an unseen recording session is therefore imperative for practical usage scenarios. We report on a meta-learning approach to pretrain a deep neural network frontend for a myoelectric speech recognizer in a way that it can be easily adapted to a new session. Fine-tuning this specially pretrained network yields lower Word Error Rates and higher frame accuracies than fine-tuning a conventionally pretrained network, without creating an increased computational burden on a possibly mobile device.
Original languageEnglish (US)
Title of host publicationGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781728127231
DOIs
StatePublished - Nov 1 2019
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

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