Papers with embedding extractor
Learning Paralinguistic Features from Audiobooks through Style Voice Conversion (2021.naacl-main)
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| Challenge: | Paralinguistics, the non-lexical components of speech, play a crucial role in human-human interaction. |
| Approach: | They propose a framework that enables a neural network to learn to extract paralinguistic attributes from speech using data that are not annotated for emotion. |
| Outcome: | The proposed framework improves on emotion recognition and speaking style detection tasks. |
Towards Speaker Verification for Crowdsourced Speech Collections (2022.lrec-1)
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| Challenge: | Existing methods to detect low quality work do not address the correctness of the data. |
| Approach: | They propose an unsupervised method for measuring speaker metadata plausibility of a collection, i.e., evaluating the match (or lack thereof) between contributors and speakers. |
| Outcome: | The proposed method shows high precision in automatically classifying contributor alignment (>0.94). |