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Posted: 18 Oct 2021 00:00

“wav2vec” October 2021 — summary from Astrophysics Data System

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“wav2vec” October 2021 — summary from Astrophysics Data System main image

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Astrophysics Data System - summary generated by Brevi Assistant


When examined on speech with undetected accents, speech recognition models frequently obtain degraded performance. In this research, we carry out systematic contrasts of DAT and MTL methods making use of a huge quantity of English accent corpus. While wav2vec 2.0 has been suggested for speech recognition, it can be used for speech emotion recognition; its performance can be significantly boosted utilizing various fine-tuning techniques. Experiments reveal that P-TAPT does far better than TAPT, particularly under low-resource setups. Wav2vec 2.0 is an end-to-end structure of self-supervised learning for speech representation that is successful in automated speech recognition, yet most of the deal with the subject has been created with a solitary language: English. In this paper, we present K-Wav2Vec 2.0, which is a modified version of Wav2vec 2.0 made for Korean automated speech recognition by discovering and enhancing numerous aspects of the original Wav2vec 2.0 Self-supervised pre-training has dramatically improved the efficiency of automated speech recognition. Experiments on ASR reveal that contrasted to wav2vec 2.0, wav2vec-S only needs marginal increment of pre-training time but might significantly enhance ASR performance on in-domain, cross-lingual and cross-domain datasets. The goal of self-supervised learning for automatic speech recognition is to learn excellent speech depictions from a large quantity of unlabeled speech for the downstream ASR task. Nonetheless, most SSL frameworks do rule out noise effectiveness, which is critical for real-world applications.


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