Abstract:Speech representations learned with self-supervised learning (SSL) have the potential to significantly improve the performance of a number of audio applications, especially when availability of labeled data from the deployment domain is limited. Despite their successes, SSL training methods are compute-and memory-heavy, and require large investments in computing infrastructure, thus putting it out of the reach of most institutions. Therefore, building efficient model architectures is essential for the wide-sca… Show more
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