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What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model
Mu Yang
, Ram C.M.C. Shekar
,
Okim Kang
, John H.L. Hansen
English
Research output
:
Contribution to journal
›
Conference article
›
peer-review
8
Scopus citations
Overview
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Dive into the research topics of 'What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model'. Together they form a unique fingerprint.
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Keyphrases
2-layer
33%
Accent Identification
100%
Automatic Speech Recognition
33%
Identification Model
100%
Learned Features
33%
Novel Words
33%
Phoneme
100%
Phoneme Representation
33%
Phonetic Information
100%
Prediction Task
33%
Prosodic Information
100%
Prosody
33%
Prosody Prediction
33%
Self-supervised Learning
33%
Self-supervised Learning Model
66%
Speech Recognition Task
33%
Transformer Layer
33%
Wav2vec 2.0
100%
Word Prosody
33%
Computer Science
Identification Model
100%
Self-Supervised Learning
100%
Speech Recognition
33%